The topic of the talk will be an in depth overview of the machine learning techniques used for audio data modeling. The focus will be on applications in the task of audio source separation.

1. THE NUTS AND BOLTS
OF MUSIC SOURCE
SEPARATION
STIPE KABIĆ

2. CONTENT
1. The problem of music source separation
1. State of the art modeling approaches
1. Practical tips and best practices
1. Further research directions and ideas

3. CONTENT
1. The problem of music source separation
1. State of the art modeling approaches
1. Practical tips and best practices
1. Further research directions and ideas

4. Music source separation is hard!

5. Multidisciplinary approach required!

6. CONTENT
1. The problem of music source separation
1. State of the art modeling approaches
1. Practical tips and best practices
1. Further research directions and ideas

7. STFT generates strong features!

8. Treat audio as a vision problem!

9. Waveform models can be better!

10. Hybrid approaches win the day!

11. CONTENT
1. The problem of music source separation
1. State of the art modeling approaches
1. Practical tips and best practices
1. Further research directions and ideas

12. Understanding audio data is key!

13. Use standard deep learning tricks!

14. MobileNet for fast idea testing!

15. CONTENT
1. The problem of music source separation
1. State of the art modeling approaches
1. Practical tips and best practices
1. Further research directions and ideas

16. Steal from Vision and NLP!

17. Look back at the fundamentals!

18. Create a novel MSS approach!

19. www.atmc.ai
info@atmc.ai