My submission for the AIcrowd music demixing challenge
- MDX Leaderboard C
- SDR scores from the AIcrowd/Sony hidden dataset:
SDR_song SDR_bass SDR_drums SDR_other SDR_vocals 9.181 10.056 9.465 6.804 10.398
- SDR scores from the AIcrowd/Sony hidden dataset:
- Models
- Ensemble of Demucs and Kuielab mdx-net
- Demucs models (all trained by meta)
- 4 x htdemucs_ft
- demucs_mmi
- a1d90b5c
- Kuielab mdx-net models (all trained by me on a single 16gb T4 GPU)
- vocals.onnx (trained with batchnorm2d, the vocals model is an onnx file because it was trained before i realized pytorch models were faster and i deleted the checkpoint)
- bass.pt (trained with groupnorm num_groups=4)
- other.pt (trained with groupnorm num_groups=2)
- drums.pt (trained with groupnorm num_groups=2)
- Things i did to boost SDR
- modify the input mixtures for each model
- input mixture for htdemucs_ft vocals = The original mixture
- input mixture for htdemucs_ft drums = Original mixture - output of htdemucs_ft vocals
- input mixture for htdemucs_ft bass = Original mixture - output of htdemucs_ft vocals + drums
- input mixture for bass, drums and other.pt = Original mixture - output of vocals.onnx
- Using original mixture - output of htdemucs_ft vocals + drums + bass as the other stem scored higher than using the actual other stem model.
- Blending the model outputs
- First i blended the demucs model outputs together, then blended the demucs outputs with the mdx-net model outputs
- [0.08, 0.08, 0.4, 0.88] was the final blend value between demucs and mdx-net (0 would mean 100% demucs was used and 1 would mean 100% mdx-net) in the order of drums,bass, other and vocals.
- modify the input mixtures for each model
https://drive.google.com/drive/folders/1ugGoKCsnRdeFjez89o4Ut_bf6wUv85wy?usp=sharing
https://github.com/rlaalstjr47
https://github.com/ws-choi
https://github.com/facebookresearch/demucs