SlideShare une entreprise Scribd logo

Sound Events and Emotions: Investigating the Relation of Rhythmic Characteristics and Arousal

•
•
Andreas Floros
Andreas Floros

A variety of recent researches in Audio Emotion Recognition (AER) outlines high performance and retrieval accuracy results. However, in most works music is considered as the original sound content that conveys the identified emotions. One of the music characteristics that is found to represent a fundamental means for conveying emotions are the rhythm- related acoustic cues. Although music is an important aspect of everyday life, there are numerous non-linguistic and non- musical sounds surrounding humans, generally defined as sound events (SEs). Despite this enormous impact of SEs to humans, a scarcity of investigations regarding AER from SEs is observed. There are only a few recent investigations concerned with SEs and AER, presenting a semantic connection between the former and the listener’s triggered emotion. In this work we analytically investigate the connection of rhythm-related characteristics of a wide range of common SEs with the arousal of the listener using sound events with semantic content. To this aim, several feature evaluation and classification tasks are conducted using different ranking and classification algorithms. High accuracy results are obtained, demonstrating a significant relation of SEs rhythmic characteristics to the elicited arousal.

TechnologieDivertissement et humour
1  sur  96
Télécharger pour lire hors ligne
Sound Events and Emotions: Investigating the Relation of Rhythmic Characteristics and Arousal K. Drossos 1 R. Kotsakis 2 G. Kalliris 2 A. Floros 1 1Digital Audio Processing & Applications Group, Audiovisual Signal Processing Laboratory, Dept. of Audiovisual Arts, Ionian University, Corfu, Greece 2Laboratory of Electronic Media, Dept. of Journalism and Mass Communication, Aristotle University of Thessaloniki, Thessaloniki, Greece
Agenda 1. Introduction Everyday life Sound and emotions 2. Objectives 3. Experimental Sequence Experimental Procedure’s Layout Sound corpus & emotional model Processing of Sounds Machine learning tests 4. Results Feature evaluation Classification 5. Discussion & Conclusions 6. Feature work
1. Introduction Everyday life Sound and emotions 2. Objectives 3. Experimental Sequence Experimental Procedure’s Layout Sound corpus & emotional model Processing of Sounds Machine learning tests 4. Results Feature evaluation Classification 5. Discussion & Conclusions 6. Feature work
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Everyday life Everyday life Stimuli Many stimuli, e.g. Visual Aural Interaction with stimuli Reactions Emotions felt
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Everyday life Everyday life Stimuli Many stimuli, e.g. Visual Aural Interaction with stimuli Reactions Emotions felt
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Everyday life Everyday life Stimuli Many stimuli, e.g. Visual Aural Interaction with stimuli Reactions Emotions felt
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Everyday life Everyday life Stimuli Many stimuli, e.g. Visual Aural Interaction with stimuli Reactions Emotions felt
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Everyday life Everyday life Stimuli Many stimuli, e.g. Visual Aural Interaction with stimuli Reactions Emotions felt
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Everyday life Everyday life Stimuli Many stimuli, e.g. Visual Aural Structured form of sound General sound Interaction with stimuli Reactions Emotions felt
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Everyday life Everyday life Stimuli Many stimuli, e.g. Visual Aural Structured form of sound General sound Interaction with stimuli Reactions Emotions felt
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions Music and emotions Music Structured form of sound Primary used to mimic and extent voice characteristics Enhancing emotion(s) conveyance Relation to emotions through (but not olny) MIR and MER Music Information Retrieval Music Emotion Recognition
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions Music and emotions Music Structured form of sound Primary used to mimic and extent voice characteristics Enhancing emotion(s) conveyance Relation to emotions through (but not olny) MIR and MER Music Information Retrieval Music Emotion Recognition
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions Music and emotions Music Structured form of sound Primary used to mimic and extent voice characteristics Enhancing emotion(s) conveyance Relation to emotions through (but not olny) MIR and MER Music Information Retrieval Music Emotion Recognition
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions Music and emotions Music Structured form of sound Primary used to mimic and extent voice characteristics Enhancing emotion(s) conveyance Relation to emotions through (but not olny) MIR and MER Music Information Retrieval Music Emotion Recognition
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions Music and emotions Music Structured form of sound Primary used to mimic and extent voice characteristics Enhancing emotion(s) conveyance Relation to emotions through (but not olny) MIR and MER Music Information Retrieval Music Emotion Recognition
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions Music and emotions Music Structured form of sound Primary used to mimic and extent voice characteristics Enhancing emotion(s) conveyance Relation to emotions through (but not olny) MIR and MER Music Information Retrieval Music Emotion Recognition
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions Music and emotions (cont’d) Research results & Applications Accuracy results up to 85% In large music data bases Opposed to typical “Artist/Genre/Year” classification Categorisation according to emotion Retrieval based on emotion Preliminary applications for synthesis of music based on emotion
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions Music and emotions (cont’d) Research results & Applications Accuracy results up to 85% In large music data bases Opposed to typical “Artist/Genre/Year” classification Categorisation according to emotion Retrieval based on emotion Preliminary applications for synthesis of music based on emotion
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions Music and emotions (cont’d) Research results & Applications Accuracy results up to 85% In large music data bases Opposed to typical “Artist/Genre/Year” classification Categorisation according to emotion Retrieval based on emotion Preliminary applications for synthesis of music based on emotion
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions Music and emotions (cont’d) Research results & Applications Accuracy results up to 85% In large music data bases Opposed to typical “Artist/Genre/Year” classification Categorisation according to emotion Retrieval based on emotion Preliminary applications for synthesis of music based on emotion
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions Music and emotions (cont’d) Research results & Applications Accuracy results up to 85% In large music data bases Opposed to typical “Artist/Genre/Year” classification Categorisation according to emotion Retrieval based on emotion Preliminary applications for synthesis of music based on emotion
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions Music and emotions (cont’d) Research results & Applications Accuracy results up to 85% In large music data bases Opposed to typical “Artist/Genre/Year” classification Categorisation according to emotion Retrieval based on emotion Preliminary applications for synthesis of music based on emotion
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions From music to sound events Sound events Non structured/general form of sound Additional information regarding: Source attributes Environment attributes Sound producing mechanism attributes Apparent almost any time, if not always, in everyday life
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions From music to sound events Sound events Non structured/general form of sound Additional information regarding: Source attributes Environment attributes Sound producing mechanism attributes Apparent almost any time, if not always, in everyday life
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions From music to sound events Sound events Non structured/general form of sound Additional information regarding: Source attributes Environment attributes Sound producing mechanism attributes Apparent almost any time, if not always, in everyday life
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions From music to sound events Sound events Non structured/general form of sound Additional information regarding: Source attributes Environment attributes Sound producing mechanism attributes Apparent almost any time, if not always, in everyday life
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions From music to sound events Sound events Non structured/general form of sound Additional information regarding: Source attributes Environment attributes Sound producing mechanism attributes Apparent almost any time, if not always, in everyday life
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions From music to sound events Sound events Non structured/general form of sound Additional information regarding: Source attributes Environment attributes Sound producing mechanism attributes Apparent almost any time, if not always, in everyday life
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions From music to sound events (cont’d) Sound events Used in many applications: Audio interactions/interfaces Video games Soundscapes Artificial acoustic environments Trigger reactions Elicit emotions
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions From music to sound events (cont’d) Sound events Used in many applications: Audio interactions/interfaces Video games Soundscapes Artificial acoustic environments Trigger reactions Elicit emotions
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions From music to sound events (cont’d) Sound events Used in many applications: Audio interactions/interfaces Video games Soundscapes Artificial acoustic environments Trigger reactions Elicit emotions
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions From music to sound events (cont’d) Sound events Used in many applications: Audio interactions/interfaces Video games Soundscapes Artificial acoustic environments Trigger reactions Elicit emotions
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions From music to sound events (cont’d) Sound events Used in many applications: Audio interactions/interfaces Video games Soundscapes Artificial acoustic environments Trigger reactions Elicit emotions
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions From music to sound events (cont’d) Sound events Used in many applications: Audio interactions/interfaces Video games Soundscapes Artificial acoustic environments Trigger reactions Elicit emotions
1. Introduction Everyday life Sound and emotions 2. Objectives 3. Experimental Sequence Experimental Procedure’s Layout Sound corpus & emotional model Processing of Sounds Machine learning tests 4. Results Feature evaluation Classification 5. Discussion & Conclusions 6. Feature work
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Objectives of current study Data Aural stimuli can elicit emotions Music is a structured form of sound Sound events are not Music’s rhythm is arousing Research question Is sound events’ rhythm arousing too?
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Objectives of current study Data Aural stimuli can elicit emotions Music is a structured form of sound Sound events are not Music’s rhythm is arousing Research question Is sound events’ rhythm arousing too?
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Objectives of current study Data Aural stimuli can elicit emotions Music is a structured form of sound Sound events are not Music’s rhythm is arousing Research question Is sound events’ rhythm arousing too?
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Objectives of current study Data Aural stimuli can elicit emotions Music is a structured form of sound Sound events are not Music’s rhythm is arousing Research question Is sound events’ rhythm arousing too?
1. Introduction Everyday life Sound and emotions 2. Objectives 3. Experimental Sequence Experimental Procedure’s Layout Sound corpus & emotional model Processing of Sounds Machine learning tests 4. Results Feature evaluation Classification 5. Discussion & Conclusions 6. Feature work
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Experimental Procedure’s Layout Layout Emotional model selection Annotated sound corpus collection Processing of sounds Pre-processing Feature extraction Machine learning tests
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Experimental Procedure’s Layout Layout Emotional model selection Annotated sound corpus collection Processing of sounds Pre-processing Feature extraction Machine learning tests
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Experimental Procedure’s Layout Layout Emotional model selection Annotated sound corpus collection Processing of sounds Pre-processing Feature extraction Machine learning tests
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Experimental Procedure’s Layout Layout Emotional model selection Annotated sound corpus collection Processing of sounds Pre-processing Feature extraction Machine learning tests
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Experimental Procedure’s Layout Layout Emotional model selection Annotated sound corpus collection Processing of sounds Pre-processing Feature extraction Machine learning tests
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Experimental Procedure’s Layout Layout Emotional model selection Annotated sound corpus collection Processing of sounds Pre-processing Feature extraction Machine learning tests
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound corpus & emotional model General Characteristics Sound Corpus International Affective Digital Sounds (IADS) 167 sounds Duration: 6 secs Variable sampling frequency Emotional model Continuous model Sounds annotated using Arousal-Valence-Dominance Self Assessment Manikin (S.A.M.) used
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound corpus & emotional model General Characteristics Sound Corpus International Affective Digital Sounds (IADS) 167 sounds Duration: 6 secs Variable sampling frequency Emotional model Continuous model Sounds annotated using Arousal-Valence-Dominance Self Assessment Manikin (S.A.M.) used
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound corpus & emotional model General Characteristics Sound Corpus International Affective Digital Sounds (IADS) 167 sounds Duration: 6 secs Variable sampling frequency Emotional model Continuous model Sounds annotated using Arousal-Valence-Dominance Self Assessment Manikin (S.A.M.) used
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound corpus & emotional model General Characteristics Sound Corpus International Affective Digital Sounds (IADS) 167 sounds Duration: 6 secs Variable sampling frequency Emotional model Continuous model Sounds annotated using Arousal-Valence-Dominance Self Assessment Manikin (S.A.M.) used
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound corpus & emotional model General Characteristics Sound Corpus International Affective Digital Sounds (IADS) 167 sounds Duration: 6 secs Variable sampling frequency Emotional model Continuous model Sounds annotated using Arousal-Valence-Dominance Self Assessment Manikin (S.A.M.) used
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound corpus & emotional model General Characteristics Sound Corpus International Affective Digital Sounds (IADS) 167 sounds Duration: 6 secs Variable sampling frequency Emotional model Continuous model Sounds annotated using Arousal-Valence-Dominance Self Assessment Manikin (S.A.M.) used
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Processing of Sounds Pre-processing General procedure Normalisation Segmentation / Windowing Clustering in two classes Class A: 24 members Class B: 143 members Segmentation/Windowing Different window lengths Ranging from 0.8 to 2.0 seconds, with 0.2 seconds increment
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Processing of Sounds Pre-processing General procedure Normalisation Segmentation / Windowing Clustering in two classes Class A: 24 members Class B: 143 members Segmentation/Windowing Different window lengths Ranging from 0.8 to 2.0 seconds, with 0.2 seconds increment
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Processing of Sounds Pre-processing General procedure Normalisation Segmentation / Windowing Clustering in two classes Class A: 24 members Class B: 143 members Segmentation/Windowing Different window lengths Ranging from 0.8 to 2.0 seconds, with 0.2 seconds increment
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Processing of Sounds Pre-processing General procedure Normalisation Segmentation / Windowing Clustering in two classes Class A: 24 members Class B: 143 members Segmentation/Windowing Different window lengths Ranging from 0.8 to 2.0 seconds, with 0.2 seconds increment
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Processing of Sounds Pre-processing General procedure Normalisation Segmentation / Windowing Clustering in two classes Class A: 24 members Class B: 143 members Segmentation/Windowing Different window lengths Ranging from 0.8 to 2.0 seconds, with 0.2 seconds increment
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Processing of Sounds Pre-processing General procedure Normalisation Segmentation / Windowing Clustering in two classes Class A: 24 members Class B: 143 members Segmentation/Windowing Different window lengths Ranging from 0.8 to 2.0 seconds, with 0.2 seconds increment
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Processing of Sounds Feature Extraction Extracted features Only rhythm related features For each segment Statistical measures Total of 26 features’ set
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Processing of Sounds Feature Extraction Extracted features Only rhythm related features For each segment Statistical measures Total of 26 features’ set
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Processing of Sounds Feature Extraction Extracted features Only rhythm related features For each segment Statistical measures Total of 26 features’ set
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Processing of Sounds Feature Extraction Extracted features Only rhythm related features For each segment Statistical measures Total of 26 features’ set
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Processing of Sounds Feature Extraction Extracted features Only rhythm related features For each segment Statistical measures Extracted Features Statistical Measures Beat spectrum Mean Onsets Standard deviation Tempo Gradient Fluctuation Kurtosis Event density Skewness Pulse clarity
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Machine learning tests Feature Evaluation Objectives Most valuable features for arousal detection Dependencies between selected features and different window lengths Algorithms used InfoGainAttributeEval SVMAttributeEval WEKA software
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Machine learning tests Feature Evaluation Objectives Most valuable features for arousal detection Dependencies between selected features and different window lengths Algorithms used InfoGainAttributeEval SVMAttributeEval WEKA software
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Machine learning tests Feature Evaluation Objectives Most valuable features for arousal detection Dependencies between selected features and different window lengths Algorithms used InfoGainAttributeEval SVMAttributeEval WEKA software
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Machine learning tests Classification Objectives Arousal classification based on rhythm Dependencies between different window lengths and classification results Algorithms used Artificial neural networks Logistic regression K Nearest Neighbors WEKA software
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Machine learning tests Classification Objectives Arousal classification based on rhythm Dependencies between different window lengths and classification results Algorithms used Artificial neural networks Logistic regression K Nearest Neighbors WEKA software
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Machine learning tests Classification Objectives Arousal classification based on rhythm Dependencies between different window lengths and classification results Algorithms used Artificial neural networks Logistic regression K Nearest Neighbors WEKA software
1. Introduction Everyday life Sound and emotions 2. Objectives 3. Experimental Sequence Experimental Procedure’s Layout Sound corpus & emotional model Processing of Sounds Machine learning tests 4. Results Feature evaluation Classification 5. Discussion & Conclusions 6. Feature work
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Feature evaluation Feature Evaluation General results Two groups of features formed, 13 features each group An upper and a lower group Inner group rank not always the same Upper and lower groups constant for all window lengths
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Feature evaluation Feature Evaluation General results Two groups of features formed, 13 features each group An upper and a lower group Inner group rank not always the same Upper and lower groups constant for all window lengths
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Feature evaluation Feature Evaluation General results Two groups of features formed, 13 features each group An upper and a lower group Inner group rank not always the same Upper and lower groups constant for all window lengths
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Feature evaluation Feature Evaluation General results Two groups of features formed, 13 features each group An upper and a lower group Inner group rank not always the same Upper and lower groups constant for all window lengths
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Feature evaluation Feature Evaluation Upper group Beatspectrum std Event density std Onsets gradient Fluctuation kurtosis Beatspectrum gradient Pulse clarity std Fluctuation mean Fluctuation std Fluctuation skewness Onsets skewness Pulse clarity kurtosis Event density kurtosis Onsets mean
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Classification Classification General results Relative un-corelation of features Variations regarding different window lengths Accuracy results Highest accuracy score: 88.37% Lowest accuracy score: 71.26%
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Classification Classification General results Relative un-corelation of features Variations regarding different window lengths Accuracy results Highest accuracy score: 88.37% Lowest accuracy score: 71.26%
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Classification Classification General results Relative un-corelation of features Variations regarding different window lengths Accuracy results Highest accuracy score: 88.37% Lowest accuracy score: 71.26%
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Classification Classification General results Relative un-corelation of features Variations regarding different window lengths Accuracy results Highest accuracy score: 88.37% Window length: 1.0 second Algorithm utilised: Logistic regression Lowest accuracy score: 71.26%
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Classification Classification General results Relative un-corelation of features Variations regarding different window lengths Accuracy results Highest accuracy score: 88.37% Window length: 1.0 second Algorithm utilised: Logistic regression Lowest accuracy score: 71.26%
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Classification Classification General results Relative un-corelation of features Variations regarding different window lengths Accuracy results Highest accuracy score: 88.37% Window length: 1.0 second Algorithm utilised: Logistic regression Lowest accuracy score: 71.26% Window length: 1.4 seconds Algorithm utilised: Artificial Neural network
1. Introduction Everyday life Sound and emotions 2. Objectives 3. Experimental Sequence Experimental Procedure’s Layout Sound corpus & emotional model Processing of Sounds Machine learning tests 4. Results Feature evaluation Classification 5. Discussion & Conclusions 6. Feature work
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Feature evaluation Features Most informative features: Rhythm’s periodicity at auditory channel (fluctuation) Onsets Event density Beatspecturm Pulse clarity Independent from semantic content
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Feature evaluation Features Most informative features: Rhythm’s periodicity at auditory channel (fluctuation) Onsets Event density Beatspecturm Pulse clarity Independent from semantic content
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Feature evaluation Features Most informative features: Rhythm’s periodicity at auditory channel (fluctuation) Onsets Event density Beatspecturm Pulse clarity Independent from semantic content
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Feature evaluation Features Most informative features: Rhythm’s periodicity at auditory channel (fluctuation) Onsets Event density Beatspecturm Pulse clarity Independent from semantic content
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Feature evaluation Features Most informative features: Rhythm’s periodicity at auditory channel (fluctuation) Onsets Event density Beatspecturm Pulse clarity Independent from semantic content
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Feature evaluation Features Most informative features: Rhythm’s periodicity at auditory channel (fluctuation) Onsets Event density Beatspecturm Pulse clarity Independent from semantic content
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Classification results Algorithms related LR’s minimum score was: 81.44% KNN’s minimum score was: 82.05% Results related Sound events’ rhythm affects arousal Thus, sound’s rhythm affect arousal
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Classification results Algorithms related LR’s minimum score was: 81.44% KNN’s minimum score was: 82.05% Results related Sound events’ rhythm affects arousal Thus, sound’s rhythm affect arousal
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Classification results Algorithms related LR’s minimum score was: 81.44% KNN’s minimum score was: 82.05% Results related Sound events’ rhythm affects arousal Thus, sound’s rhythm affect arousal
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Classification results Algorithms related LR’s minimum score was: 81.44% KNN’s minimum score was: 82.05% Results related Sound events’ rhythm affects arousal Thus, sound’s rhythm affect arousal
1. Introduction Everyday life Sound and emotions 2. Objectives 3. Experimental Sequence Experimental Procedure’s Layout Sound corpus & emotional model Processing of Sounds Machine learning tests 4. Results Feature evaluation Classification 5. Discussion & Conclusions 6. Feature work
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Feature work Features & Dimensions Other features related to arousal Connection of features with valence
Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Feature work Features & Dimensions Other features related to arousal Connection of features with valence
Thank you!

Recommandé

20211026 taicca 1 intro to mir
20211026 taicca 1 intro to mir20211026 taicca 1 intro to mir
20211026 taicca 1 intro to mir
Yi-Hsuan Yang
 
"All you need is AI and music" by Keunwoo Choi
"All you need is AI and music" by Keunwoo Choi"All you need is AI and music" by Keunwoo Choi
"All you need is AI and music" by Keunwoo Choi
Keunwoo Choi
 
Understanding Music Playlists
Understanding Music PlaylistsUnderstanding Music Playlists
Understanding Music Playlists
Keunwoo Choi
 
楊奕軒/音樂資料檢索
楊奕軒/音樂資料檢索楊奕軒/音樂資料檢索
楊奕軒/音樂資料檢索
台灣資料科學年會
 
20190625 Research at Taiwan AI Labs: Music and Speech AI
20190625 Research at Taiwan AI Labs: Music and Speech AI20190625 Research at Taiwan AI Labs: Music and Speech AI
20190625 Research at Taiwan AI Labs: Music and Speech AI
Yi-Hsuan Yang
 
20211026 taicca 2 music generation
20211026 taicca 2 music generation20211026 taicca 2 music generation
20211026 taicca 2 music generation
Yi-Hsuan Yang
 
Research on Automatic Music Composition at the Taiwan AI Labs, April 2020
Research on Automatic Music Composition at the Taiwan AI Labs, April 2020Research on Automatic Music Composition at the Taiwan AI Labs, April 2020
Research on Automatic Music Composition at the Taiwan AI Labs, April 2020
Yi-Hsuan Yang
 
Computational models of symphonic music
Computational models of symphonic musicComputational models of symphonic music
Computational models of symphonic music
Emilia Gómez
 

Recommandé

20211026 taicca 1 intro to mir
20211026 taicca 1 intro to mir20211026 taicca 1 intro to mir
20211026 taicca 1 intro to mir
Yi-Hsuan Yang
 
"All you need is AI and music" by Keunwoo Choi
"All you need is AI and music" by Keunwoo Choi"All you need is AI and music" by Keunwoo Choi
"All you need is AI and music" by Keunwoo Choi
Keunwoo Choi
 
Understanding Music Playlists
Understanding Music PlaylistsUnderstanding Music Playlists
Understanding Music Playlists
Keunwoo Choi
 
楊奕軒/音樂資料檢索
楊奕軒/音樂資料檢索楊奕軒/音樂資料檢索
楊奕軒/音樂資料檢索
台灣資料科學年會
 
20190625 Research at Taiwan AI Labs: Music and Speech AI
20190625 Research at Taiwan AI Labs: Music and Speech AI20190625 Research at Taiwan AI Labs: Music and Speech AI
20190625 Research at Taiwan AI Labs: Music and Speech AI
Yi-Hsuan Yang
 
20211026 taicca 2 music generation
20211026 taicca 2 music generation20211026 taicca 2 music generation
20211026 taicca 2 music generation
Yi-Hsuan Yang
 
Research on Automatic Music Composition at the Taiwan AI Labs, April 2020
Research on Automatic Music Composition at the Taiwan AI Labs, April 2020Research on Automatic Music Composition at the Taiwan AI Labs, April 2020
Research on Automatic Music Composition at the Taiwan AI Labs, April 2020
Yi-Hsuan Yang
 
Computational models of symphonic music
Computational models of symphonic musicComputational models of symphonic music
Computational models of symphonic music
Emilia Gómez
 
北原研究室の研究事例紹介：ベーシストの旋律分析とイコライザーの印象分析（Music×Analytics Meetup vol.5 ロングトーク）
北原研究室の研究事例紹介：ベーシストの旋律分析とイコライザーの印象分析（Music×Analytics Meetup vol.5 ロングトーク）北原研究室の研究事例紹介：ベーシストの旋律分析とイコライザーの印象分析（Music×Analytics Meetup vol.5 ロングトーク）
北原研究室の研究事例紹介：ベーシストの旋律分析とイコライザーの印象分析（Music×Analytics Meetup vol.5 ロングトーク）
kthrlab
 
machine learning x music
machine learning x musicmachine learning x music
machine learning x music
Yi-Hsuan Yang
 
Poster vega north
Poster vega northPoster vega north
Poster vega north
AcxelVega
 
Machine learning for creative AI applications in music (2018 nov)
Machine learning for creative AI applications in music (2018 nov)Machine learning for creative AI applications in music (2018 nov)
Machine learning for creative AI applications in music (2018 nov)
Yi-Hsuan Yang
 
Automatic Music Composition with Transformers, Jan 2021
Automatic Music Composition with Transformers, Jan 2021Automatic Music Composition with Transformers, Jan 2021
Automatic Music Composition with Transformers, Jan 2021
Yi-Hsuan Yang
 
Radio trailer analysis_sheet 1
Radio trailer analysis_sheet 1Radio trailer analysis_sheet 1
Radio trailer analysis_sheet 1
a2media15b
 
Unit 22 radio script
Unit 22 radio scriptUnit 22 radio script
Unit 22 radio script
KwameBoateng14
 
Learning to Generate Jazz & Pop Piano Music from Audio via MIR Techniques
Learning to Generate Jazz & Pop Piano Music from Audio via MIR TechniquesLearning to Generate Jazz & Pop Piano Music from Audio via MIR Techniques
Learning to Generate Jazz & Pop Piano Music from Audio via MIR Techniques
Yi-Hsuan Yang
 
Social Tags and Music Information Retrieval (Part I)
Social Tags and Music Information Retrieval (Part I)Social Tags and Music Information Retrieval (Part I)
Social Tags and Music Information Retrieval (Part I)
Paul Lamere
 
Machine Learning for Creative AI Applications in Music (2018 May)
Machine Learning for Creative AI Applications in Music (2018 May)Machine Learning for Creative AI Applications in Music (2018 May)
Machine Learning for Creative AI Applications in Music (2018 May)
Yi-Hsuan Yang
 
The echo nest-music_discovery(1)
The echo nest-music_discovery(1)The echo nest-music_discovery(1)
The echo nest-music_discovery(1)
Sophia Yeiji Shin
 
ISMIR 2019 tutorial: Generating music with generative adverairal networks (GANs)
ISMIR 2019 tutorial: Generating music with generative adverairal networks (GANs)ISMIR 2019 tutorial: Generating music with generative adverairal networks (GANs)
ISMIR 2019 tutorial: Generating music with generative adverairal networks (GANs)
Yi-Hsuan Yang
 
The Creative Process Behind Dialogismos I: Theoretical and Technical Consider...
The Creative Process Behind Dialogismos I: Theoretical and Technical Consider...The Creative Process Behind Dialogismos I: Theoretical and Technical Consider...
The Creative Process Behind Dialogismos I: Theoretical and Technical Consider...
Gilberto Bernardes
 
How to create a podcast
How to create a podcastHow to create a podcast
How to create a podcast
FALE - UFMG
 
Using Technology to Create Emotions
Using Technology to Create EmotionsUsing Technology to Create Emotions
Using Technology to Create Emotions
myQaa
 
TV Drama - Editing
TV Drama - EditingTV Drama - Editing
TV Drama - Editing
Zoe Lorenz
 
TV Drama - Sound
TV Drama - SoundTV Drama - Sound
TV Drama - Sound
Zoe Lorenz
 
Camera shots in tv drama
Camera shots in tv dramaCamera shots in tv drama
Camera shots in tv drama
Naamah Hill
 
Sound in TV Drama
Sound in TV DramaSound in TV Drama
Sound in TV Drama
Zoe Lorenz
 
Module 17: How Actors Create Emotions And What We Can Learn From It
Module 17: How Actors Create Emotions And What We Can Learn From ItModule 17: How Actors Create Emotions And What We Can Learn From It
Module 17: How Actors Create Emotions And What We Can Learn From It
Michael DeBlis III, Esq., LLM
 
Music 100.rtfd1__#[email protected]!#__button.gif__MACOSX.docx
Music 100.rtfd1__#[email protected]!#__button.gif__MACOSX.docxMusic 100.rtfd1__#[email protected]!#__button.gif__MACOSX.docx
Music 100.rtfd1__#[email protected]!#__button.gif__MACOSX.docx
rosemarybdodson23141
 
groman_shin_finalreport
groman_shin_finalreportgroman_shin_finalreport
groman_shin_finalreport
Daniel Shin
 

Contenu connexe

Tendances

Radio trailer analysis_sheet 1
Radio trailer analysis_sheet 1Radio trailer analysis_sheet 1
Radio trailer analysis_sheet 1
a2media15b
 
Social Tags and Music Information Retrieval (Part I)
Social Tags and Music Information Retrieval (Part I)Social Tags and Music Information Retrieval (Part I)
Social Tags and Music Information Retrieval (Part I)
Paul Lamere
 
The echo nest-music_discovery(1)
The echo nest-music_discovery(1)The echo nest-music_discovery(1)
The echo nest-music_discovery(1)
Sophia Yeiji Shin
 

Tendances (14)

北原研究室の研究事例紹介：ベーシストの旋律分析とイコライザーの印象分析（Music×Analytics Meetup vol.5 ロングトーク）
北原研究室の研究事例紹介：ベーシストの旋律分析とイコライザーの印象分析（Music×Analytics Meetup vol.5 ロングトーク）北原研究室の研究事例紹介：ベーシストの旋律分析とイコライザーの印象分析（Music×Analytics Meetup vol.5 ロングトーク）
北原研究室の研究事例紹介：ベーシストの旋律分析とイコライザーの印象分析（Music×Analytics Meetup vol.5 ロングトーク）
 
machine learning x music
machine learning x musicmachine learning x music
machine learning x music
 
Poster vega north
Poster vega northPoster vega north
Poster vega north
 
Machine learning for creative AI applications in music (2018 nov)
Machine learning for creative AI applications in music (2018 nov)Machine learning for creative AI applications in music (2018 nov)
Machine learning for creative AI applications in music (2018 nov)
 
Automatic Music Composition with Transformers, Jan 2021
Automatic Music Composition with Transformers, Jan 2021Automatic Music Composition with Transformers, Jan 2021
Automatic Music Composition with Transformers, Jan 2021
 
Radio trailer analysis_sheet 1
Radio trailer analysis_sheet 1Radio trailer analysis_sheet 1
Radio trailer analysis_sheet 1
 
Unit 22 radio script
Unit 22 radio scriptUnit 22 radio script
Unit 22 radio script
 
Learning to Generate Jazz & Pop Piano Music from Audio via MIR Techniques
Learning to Generate Jazz & Pop Piano Music from Audio via MIR TechniquesLearning to Generate Jazz & Pop Piano Music from Audio via MIR Techniques
Learning to Generate Jazz & Pop Piano Music from Audio via MIR Techniques
 
Social Tags and Music Information Retrieval (Part I)
Social Tags and Music Information Retrieval (Part I)Social Tags and Music Information Retrieval (Part I)
Social Tags and Music Information Retrieval (Part I)
 
Machine Learning for Creative AI Applications in Music (2018 May)
Machine Learning for Creative AI Applications in Music (2018 May)Machine Learning for Creative AI Applications in Music (2018 May)
Machine Learning for Creative AI Applications in Music (2018 May)
 
The echo nest-music_discovery(1)
The echo nest-music_discovery(1)The echo nest-music_discovery(1)
The echo nest-music_discovery(1)
 
ISMIR 2019 tutorial: Generating music with generative adverairal networks (GANs)
ISMIR 2019 tutorial: Generating music with generative adverairal networks (GANs)ISMIR 2019 tutorial: Generating music with generative adverairal networks (GANs)
ISMIR 2019 tutorial: Generating music with generative adverairal networks (GANs)
 
The Creative Process Behind Dialogismos I: Theoretical and Technical Consider...
The Creative Process Behind Dialogismos I: Theoretical and Technical Consider...The Creative Process Behind Dialogismos I: Theoretical and Technical Consider...
The Creative Process Behind Dialogismos I: Theoretical and Technical Consider...
 
How to create a podcast
How to create a podcastHow to create a podcast
How to create a podcast
 

En vedette

TV Drama - Editing
TV Drama - EditingTV Drama - Editing
TV Drama - Editing
Zoe Lorenz
 
TV Drama - Sound
TV Drama - SoundTV Drama - Sound
TV Drama - Sound
Zoe Lorenz
 
Camera shots in tv drama
Camera shots in tv dramaCamera shots in tv drama
Camera shots in tv drama
Naamah Hill
 
Sound in TV Drama
Sound in TV DramaSound in TV Drama
Sound in TV Drama
Zoe Lorenz
 

En vedette (6)

Using Technology to Create Emotions
Using Technology to Create EmotionsUsing Technology to Create Emotions
Using Technology to Create Emotions
 
TV Drama - Editing
TV Drama - EditingTV Drama - Editing
TV Drama - Editing
 
TV Drama - Sound
TV Drama - SoundTV Drama - Sound
TV Drama - Sound
 
Camera shots in tv drama
Camera shots in tv dramaCamera shots in tv drama
Camera shots in tv drama
 
Sound in TV Drama
Sound in TV DramaSound in TV Drama
Sound in TV Drama
 
Module 17: How Actors Create Emotions And What We Can Learn From It
Module 17: How Actors Create Emotions And What We Can Learn From ItModule 17: How Actors Create Emotions And What We Can Learn From It
Module 17: How Actors Create Emotions And What We Can Learn From It
 

Similaire à Sound Events and Emotions: Investigating the Relation of Rhythmic Characteristics and Arousal

Music 100.rtfd1__#[email protected]!#__button.gif__MACOSX.docx
Music 100.rtfd1__#[email protected]!#__button.gif__MACOSX.docxMusic 100.rtfd1__#[email protected]!#__button.gif__MACOSX.docx
Music 100.rtfd1__#[email protected]!#__button.gif__MACOSX.docx
rosemarybdodson23141
 
groman_shin_finalreport
groman_shin_finalreportgroman_shin_finalreport
groman_shin_finalreport
Daniel Shin
 
Beginning band meeting
Beginning band meetingBeginning band meeting
Beginning band meeting
IMS Bands
 
The Importance Of Enjoying Hi-Res Audio
The Importance Of Enjoying Hi-Res AudioThe Importance Of Enjoying Hi-Res Audio
The Importance Of Enjoying Hi-Res Audio
Kendra Cote
 
2 audiological evaluation
2 audiological evaluation2 audiological evaluation
2 audiological evaluation
Dr_Mo3ath
 
TRI Research Day 2015 Poster-REAL (+EV)
TRI Research Day 2015 Poster-REAL (+EV)TRI Research Day 2015 Poster-REAL (+EV)
TRI Research Day 2015 Poster-REAL (+EV)
Domenica Fanelli
 
Research Proposal
Research ProposalResearch Proposal
Research Proposal
Ryan Pelon
 
In this essay, you will write an explication of the poem I Have B.docx
In this essay, you will write an explication of the poem I Have B.docxIn this essay, you will write an explication of the poem I Have B.docx
In this essay, you will write an explication of the poem I Have B.docx
jaggernaoma
 
Musician instructor talk
Musician instructor talkMusician instructor talk
Musician instructor talk
Mark Rauterkus
 
Acoustics and basic audiometry
Acoustics and basic audiometryAcoustics and basic audiometry
Acoustics and basic audiometry
bethfernandezaud
 
Attention working memory
Attention working memoryAttention working memory
Attention working memory
혜원 정
 

Similaire à Sound Events and Emotions: Investigating the Relation of Rhythmic Characteristics and Arousal (20)

Music 100.rtfd1__#[email protected]!#__button.gif__MACOSX.docx
Music 100.rtfd1__#[email protected]!#__button.gif__MACOSX.docxMusic 100.rtfd1__#[email protected]!#__button.gif__MACOSX.docx
Music 100.rtfd1__#[email protected]!#__button.gif__MACOSX.docx
 
groman_shin_finalreport
groman_shin_finalreportgroman_shin_finalreport
groman_shin_finalreport
 
Beginning band meeting
Beginning band meetingBeginning band meeting
Beginning band meeting
 
Introduction to emotion detection
Introduction to emotion detectionIntroduction to emotion detection
Introduction to emotion detection
 
The Importance Of Enjoying Hi-Res Audio
The Importance Of Enjoying Hi-Res AudioThe Importance Of Enjoying Hi-Res Audio
The Importance Of Enjoying Hi-Res Audio
 
Sound physics
Sound physicsSound physics
Sound physics
 
The Effect of Music on Memory
The Effect of Music on MemoryThe Effect of Music on Memory
The Effect of Music on Memory
 
2 audiological evaluation
2 audiological evaluation2 audiological evaluation
2 audiological evaluation
 
TRI Research Day 2015 Poster-REAL (+EV)
TRI Research Day 2015 Poster-REAL (+EV)TRI Research Day 2015 Poster-REAL (+EV)
TRI Research Day 2015 Poster-REAL (+EV)
 
Research Proposal
Research ProposalResearch Proposal
Research Proposal
 
Acoustic analyzer
Acoustic analyzerAcoustic analyzer
Acoustic analyzer
 
AIOU Course 682 Speech And Hearing Semester Spring 2022 Assignment 1.pptx
AIOU Course 682 Speech And Hearing Semester Spring 2022 Assignment 1.pptxAIOU Course 682 Speech And Hearing Semester Spring 2022 Assignment 1.pptx
AIOU Course 682 Speech And Hearing Semester Spring 2022 Assignment 1.pptx
 
In this essay, you will write an explication of the poem I Have B.docx
In this essay, you will write an explication of the poem I Have B.docxIn this essay, you will write an explication of the poem I Have B.docx
In this essay, you will write an explication of the poem I Have B.docx
 
Automatic Speech Recognition
Automatic Speech RecognitionAutomatic Speech Recognition
Automatic Speech Recognition
 
Musician instructor talk
Musician instructor talkMusician instructor talk
Musician instructor talk
 
Acoustics and basic audiometry
Acoustics and basic audiometryAcoustics and basic audiometry
Acoustics and basic audiometry
 
Attention working memory
Attention working memoryAttention working memory
Attention working memory
 
Audio Engineering Program
Audio Engineering ProgramAudio Engineering Program
Audio Engineering Program
 
Attention and the refinement of auditory expectations: Hafter festschrift talk
Attention and the refinement of auditory expectations: Hafter festschrift talkAttention and the refinement of auditory expectations: Hafter festschrift talk
Attention and the refinement of auditory expectations: Hafter festschrift talk
 
Introduction to Language and Linguistics 003: Introduction to Phonology
Introduction to Language and Linguistics 003: Introduction to PhonologyIntroduction to Language and Linguistics 003: Introduction to Phonology
Introduction to Language and Linguistics 003: Introduction to Phonology
 

Dernier

08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
Delhi Call girls
 
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfThe Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
Enterprise Knowledge
 
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityBoost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivity
Principled Technologies
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
Safe Software
 
08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men
Delhi Call girls
 
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
Delhi Call girls
 
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of ServiceCNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Service
giselly40
 
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxThe Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptx
Malak Abu Hammad
 
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slideHistor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slide
vu2urc
 
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc
 

Dernier (20)

08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
 
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps ScriptAutomating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Script
 
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfThe Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
 
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityBoost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivity
 
Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024
 
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
 
GenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationGenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day Presentation
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
 
08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men
 
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
 
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonData Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt Robison
 
Factors to Consider When Choosing Accounts Payable Services Providers.pptx
Factors to Consider When Choosing Accounts Payable Services Providers.pptxFactors to Consider When Choosing Accounts Payable Services Providers.pptx
Factors to Consider When Choosing Accounts Payable Services Providers.pptx
 
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of ServiceCNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Service
 
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxThe Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptx
 
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slideHistor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slide
 
A Call to Action for Generative AI in 2024
A Call to Action for Generative AI in 2024A Call to Action for Generative AI in 2024
A Call to Action for Generative AI in 2024
 
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone ProcessorsExploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonets
 
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
 
Real Time Object Detection Using Open CV
Real Time Object Detection Using Open CVReal Time Object Detection Using Open CV
Real Time Object Detection Using Open CV
 

Sound Events and Emotions: Investigating the Relation of Rhythmic Characteristics and Arousal

  • 1. Sound Events and Emotions: Investigating the Relation of Rhythmic Characteristics and Arousal K. Drossos 1 R. Kotsakis 2 G. Kalliris 2 A. Floros 1 1Digital Audio Processing & Applications Group, Audiovisual Signal Processing Laboratory, Dept. of Audiovisual Arts, Ionian University, Corfu, Greece 2Laboratory of Electronic Media, Dept. of Journalism and Mass Communication, Aristotle University of Thessaloniki, Thessaloniki, Greece
  • 2. Agenda 1. Introduction Everyday life Sound and emotions 2. Objectives 3. Experimental Sequence Experimental Procedure’s Layout Sound corpus & emotional model Processing of Sounds Machine learning tests 4. Results Feature evaluation Classification 5. Discussion & Conclusions 6. Feature work
  • 3. 1. Introduction Everyday life Sound and emotions 2. Objectives 3. Experimental Sequence Experimental Procedure’s Layout Sound corpus & emotional model Processing of Sounds Machine learning tests 4. Results Feature evaluation Classification 5. Discussion & Conclusions 6. Feature work
  • 4. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Everyday life Everyday life Stimuli Many stimuli, e.g. Visual Aural Interaction with stimuli Reactions Emotions felt
  • 5. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Everyday life Everyday life Stimuli Many stimuli, e.g. Visual Aural Interaction with stimuli Reactions Emotions felt
  • 6. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Everyday life Everyday life Stimuli Many stimuli, e.g. Visual Aural Interaction with stimuli Reactions Emotions felt
  • 7. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Everyday life Everyday life Stimuli Many stimuli, e.g. Visual Aural Interaction with stimuli Reactions Emotions felt
  • 8. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Everyday life Everyday life Stimuli Many stimuli, e.g. Visual Aural Interaction with stimuli Reactions Emotions felt
  • 9. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Everyday life Everyday life Stimuli Many stimuli, e.g. Visual Aural Structured form of sound General sound Interaction with stimuli Reactions Emotions felt
  • 10. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Everyday life Everyday life Stimuli Many stimuli, e.g. Visual Aural Structured form of sound General sound Interaction with stimuli Reactions Emotions felt
  • 11. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions Music and emotions Music Structured form of sound Primary used to mimic and extent voice characteristics Enhancing emotion(s) conveyance Relation to emotions through (but not olny) MIR and MER Music Information Retrieval Music Emotion Recognition
  • 12. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions Music and emotions Music Structured form of sound Primary used to mimic and extent voice characteristics Enhancing emotion(s) conveyance Relation to emotions through (but not olny) MIR and MER Music Information Retrieval Music Emotion Recognition
  • 13. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions Music and emotions Music Structured form of sound Primary used to mimic and extent voice characteristics Enhancing emotion(s) conveyance Relation to emotions through (but not olny) MIR and MER Music Information Retrieval Music Emotion Recognition
  • 14. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions Music and emotions Music Structured form of sound Primary used to mimic and extent voice characteristics Enhancing emotion(s) conveyance Relation to emotions through (but not olny) MIR and MER Music Information Retrieval Music Emotion Recognition
  • 15. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions Music and emotions Music Structured form of sound Primary used to mimic and extent voice characteristics Enhancing emotion(s) conveyance Relation to emotions through (but not olny) MIR and MER Music Information Retrieval Music Emotion Recognition
  • 16. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions Music and emotions Music Structured form of sound Primary used to mimic and extent voice characteristics Enhancing emotion(s) conveyance Relation to emotions through (but not olny) MIR and MER Music Information Retrieval Music Emotion Recognition
  • 17. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions Music and emotions (cont’d) Research results & Applications Accuracy results up to 85% In large music data bases Opposed to typical “Artist/Genre/Year” classification Categorisation according to emotion Retrieval based on emotion Preliminary applications for synthesis of music based on emotion
  • 18. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions Music and emotions (cont’d) Research results & Applications Accuracy results up to 85% In large music data bases Opposed to typical “Artist/Genre/Year” classification Categorisation according to emotion Retrieval based on emotion Preliminary applications for synthesis of music based on emotion
  • 19. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions Music and emotions (cont’d) Research results & Applications Accuracy results up to 85% In large music data bases Opposed to typical “Artist/Genre/Year” classification Categorisation according to emotion Retrieval based on emotion Preliminary applications for synthesis of music based on emotion
  • 20. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions Music and emotions (cont’d) Research results & Applications Accuracy results up to 85% In large music data bases Opposed to typical “Artist/Genre/Year” classification Categorisation according to emotion Retrieval based on emotion Preliminary applications for synthesis of music based on emotion
  • 21. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions Music and emotions (cont’d) Research results & Applications Accuracy results up to 85% In large music data bases Opposed to typical “Artist/Genre/Year” classification Categorisation according to emotion Retrieval based on emotion Preliminary applications for synthesis of music based on emotion
  • 22. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions Music and emotions (cont’d) Research results & Applications Accuracy results up to 85% In large music data bases Opposed to typical “Artist/Genre/Year” classification Categorisation according to emotion Retrieval based on emotion Preliminary applications for synthesis of music based on emotion
  • 23. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions From music to sound events Sound events Non structured/general form of sound Additional information regarding: Source attributes Environment attributes Sound producing mechanism attributes Apparent almost any time, if not always, in everyday life
  • 24. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions From music to sound events Sound events Non structured/general form of sound Additional information regarding: Source attributes Environment attributes Sound producing mechanism attributes Apparent almost any time, if not always, in everyday life
  • 25. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions From music to sound events Sound events Non structured/general form of sound Additional information regarding: Source attributes Environment attributes Sound producing mechanism attributes Apparent almost any time, if not always, in everyday life
  • 26. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions From music to sound events Sound events Non structured/general form of sound Additional information regarding: Source attributes Environment attributes Sound producing mechanism attributes Apparent almost any time, if not always, in everyday life
  • 27. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions From music to sound events Sound events Non structured/general form of sound Additional information regarding: Source attributes Environment attributes Sound producing mechanism attributes Apparent almost any time, if not always, in everyday life
  • 28. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions From music to sound events Sound events Non structured/general form of sound Additional information regarding: Source attributes Environment attributes Sound producing mechanism attributes Apparent almost any time, if not always, in everyday life
  • 29. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions From music to sound events (cont’d) Sound events Used in many applications: Audio interactions/interfaces Video games Soundscapes Artificial acoustic environments Trigger reactions Elicit emotions
  • 30. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions From music to sound events (cont’d) Sound events Used in many applications: Audio interactions/interfaces Video games Soundscapes Artificial acoustic environments Trigger reactions Elicit emotions
  • 31. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions From music to sound events (cont’d) Sound events Used in many applications: Audio interactions/interfaces Video games Soundscapes Artificial acoustic environments Trigger reactions Elicit emotions
  • 32. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions From music to sound events (cont’d) Sound events Used in many applications: Audio interactions/interfaces Video games Soundscapes Artificial acoustic environments Trigger reactions Elicit emotions
  • 33. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions From music to sound events (cont’d) Sound events Used in many applications: Audio interactions/interfaces Video games Soundscapes Artificial acoustic environments Trigger reactions Elicit emotions
  • 34. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound and emotions From music to sound events (cont’d) Sound events Used in many applications: Audio interactions/interfaces Video games Soundscapes Artificial acoustic environments Trigger reactions Elicit emotions
  • 35. 1. Introduction Everyday life Sound and emotions 2. Objectives 3. Experimental Sequence Experimental Procedure’s Layout Sound corpus & emotional model Processing of Sounds Machine learning tests 4. Results Feature evaluation Classification 5. Discussion & Conclusions 6. Feature work
  • 36. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Objectives of current study Data Aural stimuli can elicit emotions Music is a structured form of sound Sound events are not Music’s rhythm is arousing Research question Is sound events’ rhythm arousing too?
  • 37. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Objectives of current study Data Aural stimuli can elicit emotions Music is a structured form of sound Sound events are not Music’s rhythm is arousing Research question Is sound events’ rhythm arousing too?
  • 38. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Objectives of current study Data Aural stimuli can elicit emotions Music is a structured form of sound Sound events are not Music’s rhythm is arousing Research question Is sound events’ rhythm arousing too?
  • 39. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Objectives of current study Data Aural stimuli can elicit emotions Music is a structured form of sound Sound events are not Music’s rhythm is arousing Research question Is sound events’ rhythm arousing too?
  • 40. 1. Introduction Everyday life Sound and emotions 2. Objectives 3. Experimental Sequence Experimental Procedure’s Layout Sound corpus & emotional model Processing of Sounds Machine learning tests 4. Results Feature evaluation Classification 5. Discussion & Conclusions 6. Feature work
  • 41. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Experimental Procedure’s Layout Layout Emotional model selection Annotated sound corpus collection Processing of sounds Pre-processing Feature extraction Machine learning tests
  • 42. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Experimental Procedure’s Layout Layout Emotional model selection Annotated sound corpus collection Processing of sounds Pre-processing Feature extraction Machine learning tests
  • 43. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Experimental Procedure’s Layout Layout Emotional model selection Annotated sound corpus collection Processing of sounds Pre-processing Feature extraction Machine learning tests
  • 44. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Experimental Procedure’s Layout Layout Emotional model selection Annotated sound corpus collection Processing of sounds Pre-processing Feature extraction Machine learning tests
  • 45. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Experimental Procedure’s Layout Layout Emotional model selection Annotated sound corpus collection Processing of sounds Pre-processing Feature extraction Machine learning tests
  • 46. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Experimental Procedure’s Layout Layout Emotional model selection Annotated sound corpus collection Processing of sounds Pre-processing Feature extraction Machine learning tests
  • 47. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound corpus & emotional model General Characteristics Sound Corpus International Affective Digital Sounds (IADS) 167 sounds Duration: 6 secs Variable sampling frequency Emotional model Continuous model Sounds annotated using Arousal-Valence-Dominance Self Assessment Manikin (S.A.M.) used
  • 48. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound corpus & emotional model General Characteristics Sound Corpus International Affective Digital Sounds (IADS) 167 sounds Duration: 6 secs Variable sampling frequency Emotional model Continuous model Sounds annotated using Arousal-Valence-Dominance Self Assessment Manikin (S.A.M.) used
  • 49. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound corpus & emotional model General Characteristics Sound Corpus International Affective Digital Sounds (IADS) 167 sounds Duration: 6 secs Variable sampling frequency Emotional model Continuous model Sounds annotated using Arousal-Valence-Dominance Self Assessment Manikin (S.A.M.) used
  • 50. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound corpus & emotional model General Characteristics Sound Corpus International Affective Digital Sounds (IADS) 167 sounds Duration: 6 secs Variable sampling frequency Emotional model Continuous model Sounds annotated using Arousal-Valence-Dominance Self Assessment Manikin (S.A.M.) used
  • 51. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound corpus & emotional model General Characteristics Sound Corpus International Affective Digital Sounds (IADS) 167 sounds Duration: 6 secs Variable sampling frequency Emotional model Continuous model Sounds annotated using Arousal-Valence-Dominance Self Assessment Manikin (S.A.M.) used
  • 52. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Sound corpus & emotional model General Characteristics Sound Corpus International Affective Digital Sounds (IADS) 167 sounds Duration: 6 secs Variable sampling frequency Emotional model Continuous model Sounds annotated using Arousal-Valence-Dominance Self Assessment Manikin (S.A.M.) used
  • 53. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Processing of Sounds Pre-processing General procedure Normalisation Segmentation / Windowing Clustering in two classes Class A: 24 members Class B: 143 members Segmentation/Windowing Different window lengths Ranging from 0.8 to 2.0 seconds, with 0.2 seconds increment
  • 54. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Processing of Sounds Pre-processing General procedure Normalisation Segmentation / Windowing Clustering in two classes Class A: 24 members Class B: 143 members Segmentation/Windowing Different window lengths Ranging from 0.8 to 2.0 seconds, with 0.2 seconds increment
  • 55. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Processing of Sounds Pre-processing General procedure Normalisation Segmentation / Windowing Clustering in two classes Class A: 24 members Class B: 143 members Segmentation/Windowing Different window lengths Ranging from 0.8 to 2.0 seconds, with 0.2 seconds increment
  • 56. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Processing of Sounds Pre-processing General procedure Normalisation Segmentation / Windowing Clustering in two classes Class A: 24 members Class B: 143 members Segmentation/Windowing Different window lengths Ranging from 0.8 to 2.0 seconds, with 0.2 seconds increment
  • 57. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Processing of Sounds Pre-processing General procedure Normalisation Segmentation / Windowing Clustering in two classes Class A: 24 members Class B: 143 members Segmentation/Windowing Different window lengths Ranging from 0.8 to 2.0 seconds, with 0.2 seconds increment
  • 58. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Processing of Sounds Pre-processing General procedure Normalisation Segmentation / Windowing Clustering in two classes Class A: 24 members Class B: 143 members Segmentation/Windowing Different window lengths Ranging from 0.8 to 2.0 seconds, with 0.2 seconds increment
  • 59. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Processing of Sounds Feature Extraction Extracted features Only rhythm related features For each segment Statistical measures Total of 26 features’ set
  • 60. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Processing of Sounds Feature Extraction Extracted features Only rhythm related features For each segment Statistical measures Total of 26 features’ set
  • 61. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Processing of Sounds Feature Extraction Extracted features Only rhythm related features For each segment Statistical measures Total of 26 features’ set
  • 62. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Processing of Sounds Feature Extraction Extracted features Only rhythm related features For each segment Statistical measures Total of 26 features’ set
  • 63. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Processing of Sounds Feature Extraction Extracted features Only rhythm related features For each segment Statistical measures Extracted Features Statistical Measures Beat spectrum Mean Onsets Standard deviation Tempo Gradient Fluctuation Kurtosis Event density Skewness Pulse clarity
  • 64. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Machine learning tests Feature Evaluation Objectives Most valuable features for arousal detection Dependencies between selected features and different window lengths Algorithms used InfoGainAttributeEval SVMAttributeEval WEKA software
  • 65. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Machine learning tests Feature Evaluation Objectives Most valuable features for arousal detection Dependencies between selected features and different window lengths Algorithms used InfoGainAttributeEval SVMAttributeEval WEKA software
  • 66. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Machine learning tests Feature Evaluation Objectives Most valuable features for arousal detection Dependencies between selected features and different window lengths Algorithms used InfoGainAttributeEval SVMAttributeEval WEKA software
  • 67. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Machine learning tests Classification Objectives Arousal classification based on rhythm Dependencies between different window lengths and classification results Algorithms used Artificial neural networks Logistic regression K Nearest Neighbors WEKA software
  • 68. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Machine learning tests Classification Objectives Arousal classification based on rhythm Dependencies between different window lengths and classification results Algorithms used Artificial neural networks Logistic regression K Nearest Neighbors WEKA software
  • 69. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Machine learning tests Classification Objectives Arousal classification based on rhythm Dependencies between different window lengths and classification results Algorithms used Artificial neural networks Logistic regression K Nearest Neighbors WEKA software
  • 70. 1. Introduction Everyday life Sound and emotions 2. Objectives 3. Experimental Sequence Experimental Procedure’s Layout Sound corpus & emotional model Processing of Sounds Machine learning tests 4. Results Feature evaluation Classification 5. Discussion & Conclusions 6. Feature work
  • 71. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Feature evaluation Feature Evaluation General results Two groups of features formed, 13 features each group An upper and a lower group Inner group rank not always the same Upper and lower groups constant for all window lengths
  • 72. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Feature evaluation Feature Evaluation General results Two groups of features formed, 13 features each group An upper and a lower group Inner group rank not always the same Upper and lower groups constant for all window lengths
  • 73. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Feature evaluation Feature Evaluation General results Two groups of features formed, 13 features each group An upper and a lower group Inner group rank not always the same Upper and lower groups constant for all window lengths
  • 74. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Feature evaluation Feature Evaluation General results Two groups of features formed, 13 features each group An upper and a lower group Inner group rank not always the same Upper and lower groups constant for all window lengths
  • 75. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Feature evaluation Feature Evaluation Upper group Beatspectrum std Event density std Onsets gradient Fluctuation kurtosis Beatspectrum gradient Pulse clarity std Fluctuation mean Fluctuation std Fluctuation skewness Onsets skewness Pulse clarity kurtosis Event density kurtosis Onsets mean
  • 76. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Classification Classification General results Relative un-corelation of features Variations regarding different window lengths Accuracy results Highest accuracy score: 88.37% Lowest accuracy score: 71.26%
  • 77. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Classification Classification General results Relative un-corelation of features Variations regarding different window lengths Accuracy results Highest accuracy score: 88.37% Lowest accuracy score: 71.26%
  • 78. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Classification Classification General results Relative un-corelation of features Variations regarding different window lengths Accuracy results Highest accuracy score: 88.37% Lowest accuracy score: 71.26%
  • 79. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Classification Classification General results Relative un-corelation of features Variations regarding different window lengths Accuracy results Highest accuracy score: 88.37% Window length: 1.0 second Algorithm utilised: Logistic regression Lowest accuracy score: 71.26%
  • 80. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Classification Classification General results Relative un-corelation of features Variations regarding different window lengths Accuracy results Highest accuracy score: 88.37% Window length: 1.0 second Algorithm utilised: Logistic regression Lowest accuracy score: 71.26%
  • 81. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Classification Classification General results Relative un-corelation of features Variations regarding different window lengths Accuracy results Highest accuracy score: 88.37% Window length: 1.0 second Algorithm utilised: Logistic regression Lowest accuracy score: 71.26% Window length: 1.4 seconds Algorithm utilised: Artificial Neural network
  • 82. 1. Introduction Everyday life Sound and emotions 2. Objectives 3. Experimental Sequence Experimental Procedure’s Layout Sound corpus & emotional model Processing of Sounds Machine learning tests 4. Results Feature evaluation Classification 5. Discussion & Conclusions 6. Feature work
  • 83. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Feature evaluation Features Most informative features: Rhythm’s periodicity at auditory channel (fluctuation) Onsets Event density Beatspecturm Pulse clarity Independent from semantic content
  • 84. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Feature evaluation Features Most informative features: Rhythm’s periodicity at auditory channel (fluctuation) Onsets Event density Beatspecturm Pulse clarity Independent from semantic content
  • 85. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Feature evaluation Features Most informative features: Rhythm’s periodicity at auditory channel (fluctuation) Onsets Event density Beatspecturm Pulse clarity Independent from semantic content
  • 86. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Feature evaluation Features Most informative features: Rhythm’s periodicity at auditory channel (fluctuation) Onsets Event density Beatspecturm Pulse clarity Independent from semantic content
  • 87. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Feature evaluation Features Most informative features: Rhythm’s periodicity at auditory channel (fluctuation) Onsets Event density Beatspecturm Pulse clarity Independent from semantic content
  • 88. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Feature evaluation Features Most informative features: Rhythm’s periodicity at auditory channel (fluctuation) Onsets Event density Beatspecturm Pulse clarity Independent from semantic content
  • 89. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Classification results Algorithms related LR’s minimum score was: 81.44% KNN’s minimum score was: 82.05% Results related Sound events’ rhythm affects arousal Thus, sound’s rhythm affect arousal
  • 90. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Classification results Algorithms related LR’s minimum score was: 81.44% KNN’s minimum score was: 82.05% Results related Sound events’ rhythm affects arousal Thus, sound’s rhythm affect arousal
  • 91. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Classification results Algorithms related LR’s minimum score was: 81.44% KNN’s minimum score was: 82.05% Results related Sound events’ rhythm affects arousal Thus, sound’s rhythm affect arousal
  • 92. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Classification results Algorithms related LR’s minimum score was: 81.44% KNN’s minimum score was: 82.05% Results related Sound events’ rhythm affects arousal Thus, sound’s rhythm affect arousal
  • 93. 1. Introduction Everyday life Sound and emotions 2. Objectives 3. Experimental Sequence Experimental Procedure’s Layout Sound corpus & emotional model Processing of Sounds Machine learning tests 4. Results Feature evaluation Classification 5. Discussion & Conclusions 6. Feature work
  • 94. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Feature work Features & Dimensions Other features related to arousal Connection of features with valence
  • 95. Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work Feature work Features & Dimensions Other features related to arousal Connection of features with valence