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[DSC Europe 22] The nuts and bolts of music source separation - Stipe Kabic

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[DSC Europe 22] The nuts and bolts of music source separation - Stipe Kabic

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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.

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.

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[DSC Europe 22] The nuts and bolts of music source separation - Stipe Kabic

  1. 1. THE NUTS AND BOLTS OF MUSIC SOURCE SEPARATION STIPE KABIĆ
  2. 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. 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. 4. Music source separation is hard!
  5. 5. Multidisciplinary approach required!
  6. 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. 7. STFT generates strong features!
  8. 8. Treat audio as a vision problem!
  9. 9. Waveform models can be better!
  10. 10. Hybrid approaches win the day!
  11. 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. 12. Understanding audio data is key!
  13. 13. Use standard deep learning tricks!
  14. 14. MobileNet for fast idea testing!
  15. 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. 16. Steal from Vision and NLP!
  17. 17. Look back at the fundamentals!
  18. 18. Create a novel MSS approach!
  19. 19. www.atmc.ai info@atmc.ai

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