SlideShare une entreprise Scribd logo
1  sur  29
Filtering
• Filtering is another name for
  subtractive synthesis
  because it subtracts
  frequencies from a sound
• Filtering is the opposite approach of
  additive synthesis:
  • Additive synthesis builds a complex sound
    out of sine waves.
  • Subtractive synthesis starts with a complex
    source sound and removes some of the
    frequency components.
Sound Examples
• Atlantic Brass Quintet
    • Praetorius, "Introduction" from Terpsichore:
       • 2 trumpets (high)
       • horn and trombone (medium)
       • tuba (low)
•   [iv:10] original
•   [iv:11] low-pass filtered
•   [iv:12] high-pass filtered
•   [iv:13] band-pass filtered
•   [iv:14] notch (band-stop) filtered
•   [iv:10] original
Csound Filters

• Four Main Filter Types:
  •   Low-pass — tone
  •   High-pass — atone
  •   Band-pass — reson
  •   Notch (Band-stop) — areson
Low-Pass Filter
• Very common, probably about 50% of filters
  used in computer music are low-pass.




                Frequency Response Curve
  • power = amp2; amp = sqrt(power)
  • 1/2 power = sqrt(2)/2 amp = ~71% amp
Csound Low-Pass Filter (tone)

• synthesized oboe




  [iv:15] original tone   [iv:16] low-pass filter
       261.6 Hertz             at 523.2 Hz
Csound Low-Pass Filter (tone)
• synthesized oboe with low-pass filter
;      p2      p3    p4      p5      p6     p7    p8
;      start   dur   amp     freq    attk   dec   filtfr
i10    1       3.0   10000   261.6   .045   .15   523.2


                                 ;ifiltfr=cps of response
afilt tone asig, ifiltfr         ;curve's half amp point
afilt2 tone afilt, ifiltfr       ;2nd filter =
                                 ;steeper rolloff

abal   balance   afilt2, asig    ;balance amplitude
High-Pass Filter
• Passes high frequencies, attenuates lows.
• Used to brighten a signal
  • be careful, can also increase noise
• About 20% of filters used in computer music
  are high-pass.




                         Frequency Response Curve
Csound High-Pass Filter (atone)

• synthesized oboe




   [iv:15] original tone   [iv:19] high-pass filter
        261.6 Hertz             at 1046.4 Hz
Csound High-Pass Filter (atone)
• synthesized oboe with high-pass filter
;      p2      p3    p4      p5      p6     p7    p8
;      start   dur   amp     freq    attk   dec   filtfr
i10    1       3.0   10000   261.6   .045   .15   1046.4


                                 ;ifiltfr=cps of response
afilt atone    asig, ifiltfr     ;curve's half amp point
afilt2 atone   afilt, ifiltfr    ;2nd filter =
                                 ;steeper rolloff

abal   balance   afilt2, asig    ;balance amplitude
Band-Pass Filter
• Passes band of frequencies, attenuates those
  above and below band.
• Most common in implementations of discrete
  Fourier transform to separate out harmonics.
• About 20% of filters used in computer music
  are band-pass.




                     Frequency Response Curve
Csound Band-Pass Filter (reson)
• Defined by center frequency f0, and bandwidth
  of pass-band = fhighcutoff - flowcutoff
• synthesized oboe




   [iv:15] original tone   [iv:18] b-pass filter
        261.6 Hertz         at 523.2 Hz/10 bw
Csound Band-Pass Filter (reson)

• synthesized oboe




   [iv:19] b-p filter at   [iv:20] b-p filter at
   1046.4 Hz/100 bw        1046.4 Hz/500 bw
Csound Band-Pass Filter (reson)
• synthesized oboe with band-pass filter
;       p2      p3    p4      p5      p6     p7    p8       p9
;       start   dur   amp     freq    attk   dec   filtfr   bw
i10     1       3.0   10000   261.6   .045   .15   523.2    10
i10     1       3.0   10000   261.6   .045   .15   1046.4   100
i10     1       3.0   10000   261.6   .045   .15   1046.4   500



                                ;ifiltfr=center freq of
afilt reson     asig,ifiltfr,ibw,0    ;the passband
afilt2 reson    afilt,ifiltfr,ibw,0   ;steeper rolloff

abal    balance   afilt2, asig    ;balance amplitude
Band-Stop (Notch) Filter
• Stops band of frequencies, passes those
  above and below band.
• Most common in removing electric hum (50
  Hertz A/C).
• About 10% of filters used in computer music
  are band-stop.




                     Frequency Response Curve
Csound Notch Filter (areson)
• Defined by center frequency f0, and bandwidth
  of stop-band = fhighcutoff - flowcutoff
• pulse wave




   [iv:21] original tone    [iv:22] notch filter
        261.6 Hertz             at 1046.4 Hz
                                   100 bw
Csound Notch Filter (areson)
• synthesized oboe with notch filter
;      p2      p3      p4      p5      p6     p7    p8       p9
;      start   dur     amp     freq    attk   dec   filtfr   bw
i11    1       3.0     10000   261.6   .045   .15   1046.4   100


                                ;ifiltfr=center freq of
afilt areson     asig,ifiltfr,ibw,1   ;the stopband
afilt2 areson    afilt,ifiltfr,ibw,1 ;steeper rolloff

abal   balance       afilt2, asig      ;balance amplitude

• NOTE: The fourth argument in areson is
  scaling — it must be 1 (0 default in Csound
  manual doesn't work)
LP Filter
• original synthesized oboe tone 261.6
  Hertz




  [iv:15] 0. unfiltered tone   [iv:26] 1. low-pass filter
                                       523.2 Hz
HP and BP Filter

• original synthesized oboe tone 261.6
  Hertz




   [iv:27] 2. high-pass   [iv:28] 3. band-pass
        1046.4 Hz              1046.4 Hz
Dynamically Changing the Center
         Frequency and Bandwidth
• original synthesized bassoon tone 69 Hz
• b-pass filter — freq from fundamental to harmonic 15




   [iv:23] bassoon at 69 Hz            [iv:24] bp filter 69-1035 Hz/bw 15

;     p2   p3    p4     p5    p6     p7    p8     p9     p10   p11   p12   p13
;     st   dur   amp    frq   attk   dec   flt1   flt2   bw1   bw2   wai   gls
i15   1    3     9000   69    .23    .1    69     1035   15    15    .2    .6
Dynamically Changing the Center
         Frequency and Bandwidth
• original synthesized bassoon tone 69 Hz
• band-pass filter — bw moving from 10 to 500




[iv:25] bp filter 276 Hz/bw 10-500          same — first 3 harmonics

;     p2   p3    p4     p5    p6     p7    p8     p9     p10   p11   p12   p13
;     st   dur   amp    frq   attk   dec   flt1   flt2   bw1   bw2   wai   gls
i15   1    10    9000   69    .23    .1    276    276    10    500   .2    .6
Dynamically Changing the Center
        Frequency and Bandwidth
• In the Csound manual:
ar    tone     asig,   khp[,istor]              ;l-pass
ar    atone    asig,   khp[,istor]              ;h-pass
ar    reson    asig,   kcf,kbw[,iscale,istor]   ;b-pass
ar    areson   asig,   kcf,kbw[iscale,istor]    ;notch


• Default is 0 for iscale and istor
• NOTE: Make sure that iscale is 1 if using
  the areson notch filter, as Csound
  doesn't work properly with the 0 default
Dynamically Changing the Center
       Frequency and Bandwidth
• We can change the half-power, the center
  frequency and the bandwidth at the k-rate
  using linseg statements
• original synthesized bassoon tone 69 Hz
• b-pass filter — freq from fundamental to harmonic 15

kflfr   linseg   69, idur, 1035     ;linseg for center
afilt   reson    asig,kflfr,ibw,0   ;freq of the passband

• band-pass filter — bandwidth moving from 10 to 500
kbw     linseg   10, idur, 500    ; linseg for bandwidth
afilt   reson    asig,iflfr,kbw,0      ; of the passband
Dynamically Changing the Center
      Frequency and Bandwidth
• a musical example: oboe, Bach, Fugue #2 in C Minor
• [iv:29] no filter
• [iv:30] lp filter, 55 -> 160 Hertz
• [iv:31] bp filter, 220 -> 7040 Hertz, bw 1
• [iv:32] bp filter, 220 -> 7040 Hertz, bw 1 -> 100
[iv:33] Hiss and Hum
  compare with [iv:34] 60 Hertz sine wave
• hiss
  •   high frequency noise you hear on cassette tapes
  •   unfocused — not just a single frequency
  •   which kind of filter can you use to get rid of it?
• hum
  • the noise you hear from machinery (such as lights
    and computers)
  • focused frequency, same as the local electrical
    power
  • which kind of filter can you use to get rid of it?
Filtered Noise
              with Band-Pass Filters




[iv:35] noise with bp filter at 1046.4 Hz/bw 1% of filter freq
;     p2      p3    p4     p5        p6     p7    p8
;     start   dur   amp    freq      attk   dec   bw
i16   1       5     4000   1046.4    2      2.5   .01
Filtered Noise
         with Band-Pass Filters




• [iv:36] a musical example: Ayers, Companion
  of Strange Intimacies
Filtered Noise
            with Band-Pass Filters

;noiseflt.orc

instr 16                      ; noise filter
idur        =   p3
iamp        =   p4
ifilfr      =   p5            ;filter frequency
iattack     =   p6
idecay      =   p7
ibw         =   p8 * ifreq    ;max bandwidth for filter

isus   = idur - iattack - idecay
Filtered Noise
            with Band-Pass Filters
kenv   linseg 0,iattack,1,isus,1,idecay,0,1,0   ;ampenv
knenv =       kenv * iamp     ;env for noise source
anoise rand   knenv           ;noise source
                  ;filter the noise source at ifreq
afilt reson anoise,ifreq,ibw*kenv,0,0

abal   balance   afilt, anoise ;balance amplitude
       out       abal                ;OUTPUT asig here
       endin

Contenu connexe

Tendances

Synchronous Time / Frequency Domain Measurements Using a Digital Oscilloscope...
Synchronous Time / Frequency Domain Measurements Using a Digital Oscilloscope...Synchronous Time / Frequency Domain Measurements Using a Digital Oscilloscope...
Synchronous Time / Frequency Domain Measurements Using a Digital Oscilloscope...Rohde & Schwarz North America
 
Active Filter Design Using PSpice
Active Filter Design Using PSpiceActive Filter Design Using PSpice
Active Filter Design Using PSpiceTsuyoshi Horigome
 
Musical signal processing
Musical signal processingMusical signal processing
Musical signal processingAhmed Amer
 
Week 6 assignment - Synthesis Modules
Week 6 assignment - Synthesis ModulesWeek 6 assignment - Synthesis Modules
Week 6 assignment - Synthesis Modulescmcilwraith
 
Chebyshev High Pass Filter
Chebyshev High Pass FilterChebyshev High Pass Filter
Chebyshev High Pass FilterSaikat Bhandari
 
Op amp applications filters cw final (2)
Op amp applications filters cw final (2)Op amp applications filters cw final (2)
Op amp applications filters cw final (2)JUNAID SK
 
Spectrum Analyzer Fundamentals/Advanced Spectrum Analysis
Spectrum Analyzer Fundamentals/Advanced Spectrum AnalysisSpectrum Analyzer Fundamentals/Advanced Spectrum Analysis
Spectrum Analyzer Fundamentals/Advanced Spectrum AnalysisRohde & Schwarz North America
 
Acoustic scales and levels
Acoustic scales and levelsAcoustic scales and levels
Acoustic scales and levelsFarhat Surve
 
Wideband Complex Modulation Analysis Using a Real-Time Digital Demodulator
Wideband Complex Modulation Analysis Using a Real-Time Digital DemodulatorWideband Complex Modulation Analysis Using a Real-Time Digital Demodulator
Wideband Complex Modulation Analysis Using a Real-Time Digital DemodulatorRohde & Schwarz North America
 
An Gt123 A Electronic Step Attenuator For Microwave Signal Generators
An Gt123 A Electronic Step Attenuator For Microwave Signal GeneratorsAn Gt123 A Electronic Step Attenuator For Microwave Signal Generators
An Gt123 A Electronic Step Attenuator For Microwave Signal Generatorscf_home
 

Tendances (20)

Synchronous Time / Frequency Domain Measurements Using a Digital Oscilloscope...
Synchronous Time / Frequency Domain Measurements Using a Digital Oscilloscope...Synchronous Time / Frequency Domain Measurements Using a Digital Oscilloscope...
Synchronous Time / Frequency Domain Measurements Using a Digital Oscilloscope...
 
Active Filter Design Using PSpice
Active Filter Design Using PSpiceActive Filter Design Using PSpice
Active Filter Design Using PSpice
 
Musical signal processing
Musical signal processingMusical signal processing
Musical signal processing
 
Active Filters
Active FiltersActive Filters
Active Filters
 
Week 6 assignment - Synthesis Modules
Week 6 assignment - Synthesis ModulesWeek 6 assignment - Synthesis Modules
Week 6 assignment - Synthesis Modules
 
Chebyshev High Pass Filter
Chebyshev High Pass FilterChebyshev High Pass Filter
Chebyshev High Pass Filter
 
Compression
CompressionCompression
Compression
 
Op amp applications filters cw final (2)
Op amp applications filters cw final (2)Op amp applications filters cw final (2)
Op amp applications filters cw final (2)
 
Measuring Jitter Using Phase Noise Techniques
Measuring Jitter Using Phase Noise TechniquesMeasuring Jitter Using Phase Noise Techniques
Measuring Jitter Using Phase Noise Techniques
 
Active filter
Active filterActive filter
Active filter
 
filters
filtersfilters
filters
 
Evs1
Evs1Evs1
Evs1
 
Equalisers
EqualisersEqualisers
Equalisers
 
Spectrum Analyzer Fundamentals/Advanced Spectrum Analysis
Spectrum Analyzer Fundamentals/Advanced Spectrum AnalysisSpectrum Analyzer Fundamentals/Advanced Spectrum Analysis
Spectrum Analyzer Fundamentals/Advanced Spectrum Analysis
 
Acoustic scales and levels
Acoustic scales and levelsAcoustic scales and levels
Acoustic scales and levels
 
Doppler
DopplerDoppler
Doppler
 
11 hp filter
11 hp filter11 hp filter
11 hp filter
 
Wideband Complex Modulation Analysis Using a Real-Time Digital Demodulator
Wideband Complex Modulation Analysis Using a Real-Time Digital DemodulatorWideband Complex Modulation Analysis Using a Real-Time Digital Demodulator
Wideband Complex Modulation Analysis Using a Real-Time Digital Demodulator
 
An Gt123 A Electronic Step Attenuator For Microwave Signal Generators
An Gt123 A Electronic Step Attenuator For Microwave Signal GeneratorsAn Gt123 A Electronic Step Attenuator For Microwave Signal Generators
An Gt123 A Electronic Step Attenuator For Microwave Signal Generators
 
Filters
FiltersFilters
Filters
 

En vedette

Dobule and triple integral
Dobule and triple integralDobule and triple integral
Dobule and triple integralsonendra Gupta
 
Multiple integral(tripple integral)
Multiple integral(tripple integral)Multiple integral(tripple integral)
Multiple integral(tripple integral)jigar sable
 
Operational Amplifier (OpAmp)
Operational Amplifier (OpAmp)Operational Amplifier (OpAmp)
Operational Amplifier (OpAmp)Mohammed Bamatraf
 
Operational Amplifier Part 1
Operational Amplifier Part 1Operational Amplifier Part 1
Operational Amplifier Part 1Mukesh Tekwani
 
Op amp(operational amplifier)
Op amp(operational amplifier)Op amp(operational amplifier)
Op amp(operational amplifier)Kausik das
 

En vedette (6)

7
77
7
 
Dobule and triple integral
Dobule and triple integralDobule and triple integral
Dobule and triple integral
 
Multiple integral(tripple integral)
Multiple integral(tripple integral)Multiple integral(tripple integral)
Multiple integral(tripple integral)
 
Operational Amplifier (OpAmp)
Operational Amplifier (OpAmp)Operational Amplifier (OpAmp)
Operational Amplifier (OpAmp)
 
Operational Amplifier Part 1
Operational Amplifier Part 1Operational Amplifier Part 1
Operational Amplifier Part 1
 
Op amp(operational amplifier)
Op amp(operational amplifier)Op amp(operational amplifier)
Op amp(operational amplifier)
 

Similaire à Filters unit iii (20)

filters.ppt
filters.pptfilters.ppt
filters.ppt
 
Active_Filter.pptx.pdffdffgygydhdudyddyxyddyd
Active_Filter.pptx.pdffdffgygydhdudyddyxyddydActive_Filter.pptx.pdffdffgygydhdudyddyxyddyd
Active_Filter.pptx.pdffdffgygydhdudyddyxyddyd
 
3 (1)
3 (1)3 (1)
3 (1)
 
Filters 2
Filters 2Filters 2
Filters 2
 
Filters
FiltersFilters
Filters
 
Virtual Analog Phasers
Virtual Analog PhasersVirtual Analog Phasers
Virtual Analog Phasers
 
3 (2)
3 (2)3 (2)
3 (2)
 
Cauan
CauanCauan
Cauan
 
Bani
BaniBani
Bani
 
3 (3)
3 (3)3 (3)
3 (3)
 
Agdon
AgdonAgdon
Agdon
 
dennis fisher pll
dennis fisher pll dennis fisher pll
dennis fisher pll
 
Pula
PulaPula
Pula
 
Pagara
PagaraPagara
Pagara
 
Exp passive filter (5)
Exp passive filter (5)Exp passive filter (5)
Exp passive filter (5)
 
waveforms.ppt
waveforms.pptwaveforms.ppt
waveforms.ppt
 
Comm8(exp.3)
Comm8(exp.3)Comm8(exp.3)
Comm8(exp.3)
 
Maala
MaalaMaala
Maala
 
National college of science and technology
National college of science and technologyNational college of science and technology
National college of science and technology
 
Wavelet Transform and DSP Applications
Wavelet Transform and DSP ApplicationsWavelet Transform and DSP Applications
Wavelet Transform and DSP Applications
 

Plus de mrecedu

Brochure final
Brochure finalBrochure final
Brochure finalmrecedu
 
Attenuator unit iv
Attenuator unit ivAttenuator unit iv
Attenuator unit ivmrecedu
 
Two port networks unit ii
Two port networks unit iiTwo port networks unit ii
Two port networks unit iimrecedu
 
Unit4 (2)
Unit4 (2)Unit4 (2)
Unit4 (2)mrecedu
 
Unit5 (2)
Unit5 (2)Unit5 (2)
Unit5 (2)mrecedu
 
Unit6 jwfiles
Unit6 jwfilesUnit6 jwfiles
Unit6 jwfilesmrecedu
 
Unit3 jwfiles
Unit3 jwfilesUnit3 jwfiles
Unit3 jwfilesmrecedu
 
Unit2 jwfiles
Unit2 jwfilesUnit2 jwfiles
Unit2 jwfilesmrecedu
 
Unit1 jwfiles
Unit1 jwfilesUnit1 jwfiles
Unit1 jwfilesmrecedu
 
Unit7 jwfiles
Unit7 jwfilesUnit7 jwfiles
Unit7 jwfilesmrecedu
 
M1 unit vi-jntuworld
M1 unit vi-jntuworldM1 unit vi-jntuworld
M1 unit vi-jntuworldmrecedu
 
M1 unit v-jntuworld
M1 unit v-jntuworldM1 unit v-jntuworld
M1 unit v-jntuworldmrecedu
 
M1 unit iv-jntuworld
M1 unit iv-jntuworldM1 unit iv-jntuworld
M1 unit iv-jntuworldmrecedu
 
M1 unit iii-jntuworld
M1 unit iii-jntuworldM1 unit iii-jntuworld
M1 unit iii-jntuworldmrecedu
 
M1 unit ii-jntuworld
M1 unit ii-jntuworldM1 unit ii-jntuworld
M1 unit ii-jntuworldmrecedu
 
M1 unit i-jntuworld
M1 unit i-jntuworldM1 unit i-jntuworld
M1 unit i-jntuworldmrecedu
 

Plus de mrecedu (20)

Brochure final
Brochure finalBrochure final
Brochure final
 
Unit i
Unit iUnit i
Unit i
 
Attenuator unit iv
Attenuator unit ivAttenuator unit iv
Attenuator unit iv
 
Two port networks unit ii
Two port networks unit iiTwo port networks unit ii
Two port networks unit ii
 
Unit 8
Unit 8Unit 8
Unit 8
 
Unit4 (2)
Unit4 (2)Unit4 (2)
Unit4 (2)
 
Unit5
Unit5Unit5
Unit5
 
Unit4
Unit4Unit4
Unit4
 
Unit5 (2)
Unit5 (2)Unit5 (2)
Unit5 (2)
 
Unit6 jwfiles
Unit6 jwfilesUnit6 jwfiles
Unit6 jwfiles
 
Unit3 jwfiles
Unit3 jwfilesUnit3 jwfiles
Unit3 jwfiles
 
Unit2 jwfiles
Unit2 jwfilesUnit2 jwfiles
Unit2 jwfiles
 
Unit1 jwfiles
Unit1 jwfilesUnit1 jwfiles
Unit1 jwfiles
 
Unit7 jwfiles
Unit7 jwfilesUnit7 jwfiles
Unit7 jwfiles
 
M1 unit vi-jntuworld
M1 unit vi-jntuworldM1 unit vi-jntuworld
M1 unit vi-jntuworld
 
M1 unit v-jntuworld
M1 unit v-jntuworldM1 unit v-jntuworld
M1 unit v-jntuworld
 
M1 unit iv-jntuworld
M1 unit iv-jntuworldM1 unit iv-jntuworld
M1 unit iv-jntuworld
 
M1 unit iii-jntuworld
M1 unit iii-jntuworldM1 unit iii-jntuworld
M1 unit iii-jntuworld
 
M1 unit ii-jntuworld
M1 unit ii-jntuworldM1 unit ii-jntuworld
M1 unit ii-jntuworld
 
M1 unit i-jntuworld
M1 unit i-jntuworldM1 unit i-jntuworld
M1 unit i-jntuworld
 

Dernier

Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 3652toLead Limited
 
Story boards and shot lists for my a level piece
Story boards and shot lists for my a level pieceStory boards and shot lists for my a level piece
Story boards and shot lists for my a level piececharlottematthew16
 
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage CostLeverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage CostZilliz
 
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Mark Simos
 
Commit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyCommit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyAlfredo García Lavilla
 
Unraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfUnraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfAlex Barbosa Coqueiro
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek SchlawackFwdays
 
Unleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubUnleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubKalema Edgar
 
Artificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxArtificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxhariprasad279825
 
Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Mattias Andersson
 
Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024Enterprise Knowledge
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxMerck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxLoriGlavin3
 
Human Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsHuman Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsMark Billinghurst
 
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii SoldatenkoFwdays
 
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdfHyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdfPrecisely
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLScyllaDB
 
From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .Alan Dix
 
What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024Stephanie Beckett
 

Dernier (20)

Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365
 
Story boards and shot lists for my a level piece
Story boards and shot lists for my a level pieceStory boards and shot lists for my a level piece
Story boards and shot lists for my a level piece
 
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage CostLeverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
 
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptxE-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
 
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
 
Commit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyCommit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easy
 
Unraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfUnraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdf
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
 
Unleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubUnleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding Club
 
Artificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxArtificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptx
 
Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?
 
Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxMerck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
 
Human Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsHuman Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR Systems
 
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko
 
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdfHyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQL
 
From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .
 
What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024
 

Filters unit iii

  • 1.
  • 2. Filtering • Filtering is another name for subtractive synthesis because it subtracts frequencies from a sound • Filtering is the opposite approach of additive synthesis: • Additive synthesis builds a complex sound out of sine waves. • Subtractive synthesis starts with a complex source sound and removes some of the frequency components.
  • 3. Sound Examples • Atlantic Brass Quintet • Praetorius, "Introduction" from Terpsichore: • 2 trumpets (high) • horn and trombone (medium) • tuba (low) • [iv:10] original • [iv:11] low-pass filtered • [iv:12] high-pass filtered • [iv:13] band-pass filtered • [iv:14] notch (band-stop) filtered • [iv:10] original
  • 4. Csound Filters • Four Main Filter Types: • Low-pass — tone • High-pass — atone • Band-pass — reson • Notch (Band-stop) — areson
  • 5. Low-Pass Filter • Very common, probably about 50% of filters used in computer music are low-pass. Frequency Response Curve • power = amp2; amp = sqrt(power) • 1/2 power = sqrt(2)/2 amp = ~71% amp
  • 6. Csound Low-Pass Filter (tone) • synthesized oboe [iv:15] original tone [iv:16] low-pass filter 261.6 Hertz at 523.2 Hz
  • 7. Csound Low-Pass Filter (tone) • synthesized oboe with low-pass filter ; p2 p3 p4 p5 p6 p7 p8 ; start dur amp freq attk dec filtfr i10 1 3.0 10000 261.6 .045 .15 523.2 ;ifiltfr=cps of response afilt tone asig, ifiltfr ;curve's half amp point afilt2 tone afilt, ifiltfr ;2nd filter = ;steeper rolloff abal balance afilt2, asig ;balance amplitude
  • 8. High-Pass Filter • Passes high frequencies, attenuates lows. • Used to brighten a signal • be careful, can also increase noise • About 20% of filters used in computer music are high-pass. Frequency Response Curve
  • 9. Csound High-Pass Filter (atone) • synthesized oboe [iv:15] original tone [iv:19] high-pass filter 261.6 Hertz at 1046.4 Hz
  • 10. Csound High-Pass Filter (atone) • synthesized oboe with high-pass filter ; p2 p3 p4 p5 p6 p7 p8 ; start dur amp freq attk dec filtfr i10 1 3.0 10000 261.6 .045 .15 1046.4 ;ifiltfr=cps of response afilt atone asig, ifiltfr ;curve's half amp point afilt2 atone afilt, ifiltfr ;2nd filter = ;steeper rolloff abal balance afilt2, asig ;balance amplitude
  • 11. Band-Pass Filter • Passes band of frequencies, attenuates those above and below band. • Most common in implementations of discrete Fourier transform to separate out harmonics. • About 20% of filters used in computer music are band-pass. Frequency Response Curve
  • 12. Csound Band-Pass Filter (reson) • Defined by center frequency f0, and bandwidth of pass-band = fhighcutoff - flowcutoff • synthesized oboe [iv:15] original tone [iv:18] b-pass filter 261.6 Hertz at 523.2 Hz/10 bw
  • 13. Csound Band-Pass Filter (reson) • synthesized oboe [iv:19] b-p filter at [iv:20] b-p filter at 1046.4 Hz/100 bw 1046.4 Hz/500 bw
  • 14. Csound Band-Pass Filter (reson) • synthesized oboe with band-pass filter ; p2 p3 p4 p5 p6 p7 p8 p9 ; start dur amp freq attk dec filtfr bw i10 1 3.0 10000 261.6 .045 .15 523.2 10 i10 1 3.0 10000 261.6 .045 .15 1046.4 100 i10 1 3.0 10000 261.6 .045 .15 1046.4 500 ;ifiltfr=center freq of afilt reson asig,ifiltfr,ibw,0 ;the passband afilt2 reson afilt,ifiltfr,ibw,0 ;steeper rolloff abal balance afilt2, asig ;balance amplitude
  • 15. Band-Stop (Notch) Filter • Stops band of frequencies, passes those above and below band. • Most common in removing electric hum (50 Hertz A/C). • About 10% of filters used in computer music are band-stop. Frequency Response Curve
  • 16. Csound Notch Filter (areson) • Defined by center frequency f0, and bandwidth of stop-band = fhighcutoff - flowcutoff • pulse wave [iv:21] original tone [iv:22] notch filter 261.6 Hertz at 1046.4 Hz 100 bw
  • 17. Csound Notch Filter (areson) • synthesized oboe with notch filter ; p2 p3 p4 p5 p6 p7 p8 p9 ; start dur amp freq attk dec filtfr bw i11 1 3.0 10000 261.6 .045 .15 1046.4 100 ;ifiltfr=center freq of afilt areson asig,ifiltfr,ibw,1 ;the stopband afilt2 areson afilt,ifiltfr,ibw,1 ;steeper rolloff abal balance afilt2, asig ;balance amplitude • NOTE: The fourth argument in areson is scaling — it must be 1 (0 default in Csound manual doesn't work)
  • 18. LP Filter • original synthesized oboe tone 261.6 Hertz [iv:15] 0. unfiltered tone [iv:26] 1. low-pass filter 523.2 Hz
  • 19. HP and BP Filter • original synthesized oboe tone 261.6 Hertz [iv:27] 2. high-pass [iv:28] 3. band-pass 1046.4 Hz 1046.4 Hz
  • 20. Dynamically Changing the Center Frequency and Bandwidth • original synthesized bassoon tone 69 Hz • b-pass filter — freq from fundamental to harmonic 15 [iv:23] bassoon at 69 Hz [iv:24] bp filter 69-1035 Hz/bw 15 ; p2 p3 p4 p5 p6 p7 p8 p9 p10 p11 p12 p13 ; st dur amp frq attk dec flt1 flt2 bw1 bw2 wai gls i15 1 3 9000 69 .23 .1 69 1035 15 15 .2 .6
  • 21. Dynamically Changing the Center Frequency and Bandwidth • original synthesized bassoon tone 69 Hz • band-pass filter — bw moving from 10 to 500 [iv:25] bp filter 276 Hz/bw 10-500 same — first 3 harmonics ; p2 p3 p4 p5 p6 p7 p8 p9 p10 p11 p12 p13 ; st dur amp frq attk dec flt1 flt2 bw1 bw2 wai gls i15 1 10 9000 69 .23 .1 276 276 10 500 .2 .6
  • 22. Dynamically Changing the Center Frequency and Bandwidth • In the Csound manual: ar tone asig, khp[,istor] ;l-pass ar atone asig, khp[,istor] ;h-pass ar reson asig, kcf,kbw[,iscale,istor] ;b-pass ar areson asig, kcf,kbw[iscale,istor] ;notch • Default is 0 for iscale and istor • NOTE: Make sure that iscale is 1 if using the areson notch filter, as Csound doesn't work properly with the 0 default
  • 23. Dynamically Changing the Center Frequency and Bandwidth • We can change the half-power, the center frequency and the bandwidth at the k-rate using linseg statements • original synthesized bassoon tone 69 Hz • b-pass filter — freq from fundamental to harmonic 15 kflfr linseg 69, idur, 1035 ;linseg for center afilt reson asig,kflfr,ibw,0 ;freq of the passband • band-pass filter — bandwidth moving from 10 to 500 kbw linseg 10, idur, 500 ; linseg for bandwidth afilt reson asig,iflfr,kbw,0 ; of the passband
  • 24. Dynamically Changing the Center Frequency and Bandwidth • a musical example: oboe, Bach, Fugue #2 in C Minor • [iv:29] no filter • [iv:30] lp filter, 55 -> 160 Hertz • [iv:31] bp filter, 220 -> 7040 Hertz, bw 1 • [iv:32] bp filter, 220 -> 7040 Hertz, bw 1 -> 100
  • 25. [iv:33] Hiss and Hum compare with [iv:34] 60 Hertz sine wave • hiss • high frequency noise you hear on cassette tapes • unfocused — not just a single frequency • which kind of filter can you use to get rid of it? • hum • the noise you hear from machinery (such as lights and computers) • focused frequency, same as the local electrical power • which kind of filter can you use to get rid of it?
  • 26. Filtered Noise with Band-Pass Filters [iv:35] noise with bp filter at 1046.4 Hz/bw 1% of filter freq ; p2 p3 p4 p5 p6 p7 p8 ; start dur amp freq attk dec bw i16 1 5 4000 1046.4 2 2.5 .01
  • 27. Filtered Noise with Band-Pass Filters • [iv:36] a musical example: Ayers, Companion of Strange Intimacies
  • 28. Filtered Noise with Band-Pass Filters ;noiseflt.orc instr 16 ; noise filter idur = p3 iamp = p4 ifilfr = p5 ;filter frequency iattack = p6 idecay = p7 ibw = p8 * ifreq ;max bandwidth for filter isus = idur - iattack - idecay
  • 29. Filtered Noise with Band-Pass Filters kenv linseg 0,iattack,1,isus,1,idecay,0,1,0 ;ampenv knenv = kenv * iamp ;env for noise source anoise rand knenv ;noise source ;filter the noise source at ifreq afilt reson anoise,ifreq,ibw*kenv,0,0 abal balance afilt, anoise ;balance amplitude out abal ;OUTPUT asig here endin