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MDX challenge 2021 town hall presentation

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Chin Yun Yu

My slides use in mdx 2021 town hall online meeting.

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MDX 2021 Town Hall Presentation Chin-Yun Yu, Kin-Wai Cheuk 8/22 1
About Us Chin-Yun Yu ● Organizer of the team Kazane_Ryo_no_Danna ● Software Engineer, Rayark (current position) ● Engineer, Vive R&D/Multi Media, HTC ● Research Assistant, Music and Culture Technology Lab, Institute of Information Science, Academia Sinica Kin-Wai Chuek ● PhD candidate, Singapore University of Technology and Design 2
Motivation ● Try out new ideas ● Get familiar with different types of MIR topics ● Test our limits ● GET THE PRIZE 3
Final rank ● 4th place on leaderboard A ● 6th place on leaderboard B 4
Our Approach 5
Model Fusion ● Blending outputs from three different model linearly ● Round 2 score: 6.85 Drums Bass Other Vocals Model 1 (frequency masking) 0.2 0.2 0 0.2 Model 2 (frequency masking) 0.2 0.17 0.5 0.4 Model 3 (time domain) 0.6 0.73 0.5 0.4 6
Model 1: Improving X-UMX ● Two tiny tweaks on the loss function ○ Calculate the frequency-domain mse loss (Eq. 4a in [1]) using complex value instead of absolute value ○ Apply one iteration of Multichannel Wiener Filtering (MWF) before calculating the loss ■ Our differentiable norbert package: https://github.com/yoyololicon/norbert/tree/batch-support ○ Round 2 score: 5.5(baseline) => 5.64 (complex mse + MWF) [1] Sawata, Ryosuke, et al. "All for One and One for All: Improving Music Separation by Bridging Networks." ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021. 7
Model 2: U-Net ● Adopt the U-Net structure from Spleeter ● Replace each convolution block with a D3 Block from D3Net ● Add two local attention layers as the bottleneck layer ● Round 2 score: 6.03 ● We also experimented with biaxial biLSTMs (time and frequency axes) ● Round 2 score: 5.85 2D local attention x2 8
Model 3: Improving Demus ● Cropping mechanism ○ The original Demucs uses center-crop to connect skipped layers when doing upsampling, which might introduce some time shifting between layers ○ We neglectthose extra values at the end of the sequences skipped layer hidden layer skipped layer hidden layer 9
Model 3: Improving Demus ● Using 4 independent decoders, each of which corresponds to one source ○ We adjust the channel size of the decoders to have a comparable number of parameters compared to the original version ● Round 2 score with these two modifications: 6.07 (baseline) => 6.27 LSTM x2 LSTM x2 Enc Dec Enc 10
General Impressions 11
What we liked ● musdb dataloader is pretty slow ● Unexpected server error ● Cannot change team name after creation 12 ● Friendly community ● Instant help from the support team What could be improved
Acknowledgement ● Sung-Lin Yeh ● Showmin Wang ● Yu-Te Wu ● Yin-Jyun Luo 13

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MDX challenge 2021 town hall presentation

  • 1. MDX 2021 Town Hall Presentation Chin-Yun Yu, Kin-Wai Cheuk 8/22 1
  • 2. About Us Chin-Yun Yu ● Organizer of the team Kazane_Ryo_no_Danna ● Software Engineer, Rayark (current position) ● Engineer, Vive R&D/Multi Media, HTC ● Research Assistant, Music and Culture Technology Lab, Institute of Information Science, Academia Sinica Kin-Wai Chuek ● PhD candidate, Singapore University of Technology and Design 2
  • 3. Motivation ● Try out new ideas ● Get familiar with different types of MIR topics ● Test our limits ● GET THE PRIZE 3
  • 4. Final rank ● 4th place on leaderboard A ● 6th place on leaderboard B 4
  • 6. Model Fusion ● Blending outputs from three different model linearly ● Round 2 score: 6.85 Drums Bass Other Vocals Model 1 (frequency masking) 0.2 0.2 0 0.2 Model 2 (frequency masking) 0.2 0.17 0.5 0.4 Model 3 (time domain) 0.6 0.73 0.5 0.4 6
  • 7. Model 1: Improving X-UMX ● Two tiny tweaks on the loss function ○ Calculate the frequency-domain mse loss (Eq. 4a in [1]) using complex value instead of absolute value ○ Apply one iteration of Multichannel Wiener Filtering (MWF) before calculating the loss ■ Our differentiable norbert package: https://github.com/yoyololicon/norbert/tree/batch-support ○ Round 2 score: 5.5(baseline) => 5.64 (complex mse + MWF) [1] Sawata, Ryosuke, et al. "All for One and One for All: Improving Music Separation by Bridging Networks." ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021. 7
  • 8. Model 2: U-Net ● Adopt the U-Net structure from Spleeter ● Replace each convolution block with a D3 Block from D3Net ● Add two local attention layers as the bottleneck layer ● Round 2 score: 6.03 ● We also experimented with biaxial biLSTMs (time and frequency axes) ● Round 2 score: 5.85 2D local attention x2 8
  • 9. Model 3: Improving Demus ● Cropping mechanism ○ The original Demucs uses center-crop to connect skipped layers when doing upsampling, which might introduce some time shifting between layers ○ We neglectthose extra values at the end of the sequences skipped layer hidden layer skipped layer hidden layer 9
  • 10. Model 3: Improving Demus ● Using 4 independent decoders, each of which corresponds to one source ○ We adjust the channel size of the decoders to have a comparable number of parameters compared to the original version ● Round 2 score with these two modifications: 6.07 (baseline) => 6.27 LSTM x2 LSTM x2 Enc Dec Enc 10
  • 12. What we liked ● musdb dataloader is pretty slow ● Unexpected server error ● Cannot change team name after creation 12 ● Friendly community ● Instant help from the support team What could be improved
  • 13. Acknowledgement ● Sung-Lin Yeh ● Showmin Wang ● Yu-Te Wu ● Yin-Jyun Luo 13