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![Music Demixing Banner](https://images.aicrowd.com/raw_images/challenges/social_media_image_file/777/8be36d177c2b161d7944.jpg)
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# [Music Demixing Challenge ](https://www.aicrowd.com/challenges/music-demixing-challenge-ismir-2021)- xumx-sliCQ
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This repository is the GitHub mirror of my working submission repository for the AICrowd ISMIR 2021 Music Demixing Challenge (MDX): https://gitlab.aicrowd.com/sevagh/music-demixing-challenge-starter-kit
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![mixdemix](./docs/mixdemix.png)
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**:tada: !HUGE SHOUTOUT! :tada:** to the organizers who made a cool competition experience, and my peers who submitted some excellent models. I learned a lot about music demixing.
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Related links to my submission are:
* Clean repository for my neural network, https://github.com/sevagh/xumx-sliCQ - to make submissions, I copied the code and trained models from the xumx-sliCQ project into this one
* PyTorch sliCQ Transform: https://github.com/sevagh/nsgt
* My post-competition presentation: YouTube recording [▶️](https://youtu.be/TntPVZ4ajIk?t=2448), slides [📚](./docs/mdx_townhall_aug21.pdf)
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Here's a summary of (what I consider to be) interesting tagged submissions, starting from newest to oldest:
* Wiener-EM on zero-padded sliCQ, still too slow: https://github.com/sevagh/music-demixing-challenge-ismir-2021/commit/9e9f80c5664bad154a56a5bf885d3584e4e8bd5e
* Wiener-EM on sliCQ, too slow: https://github.com/sevagh/music-demixing-challenge-ismir-2021/commit/ebae8aa24979eece02cf88ffae01e991d7410965 
* Dilated convolutions for faster inference: https://github.com/sevagh/music-demixing-challenge-ismir-2021/commit/bfbccf9323692da6b3ebf623d1b0366b65ba50c3
* Bandwidth model, where frequency bins above 16000 Hz are ignored (those sliCQT bins pass through the network unmodified): https://github.com/sevagh/music-demixing-challenge-ismir-2021/commit/b80d56266b9a630775a44843c4a124227a20a738
* First time switching to CrossNet-UMX (X-UMX): https://github.com/sevagh/music-demixing-challenge-ismir-2021/commit/d2784c89d217f12b66422121db0eaa7ed3c751ca
* Use Wiener-EM with the STFT instead of no EM step: https://github.com/sevagh/music-demixing-challenge-ismir-2021/commit/55f85db110aa57fbe3ab21247d76e3e82b67d544
* One of the very first successful models, pre-XUMX, called "umx-sliCQ": https://github.com/sevagh/music-demixing-challenge-ismir-2021/commit/2cee876dc092a8c52f249440cadf018b6b16cbe3
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I tried many ideas over the course of the competition. I discarded some as hard to explain or train, but they might still be worthwhile. You can see a "scrapyard" of my various abandoned ideas: https://gitlab.com/sevagh/xumx_slicq_extra/-/tree/main/umx_experiments. From these, my favorites are:
* 3D convolutions on the `(slice x time x frequency)` sliCQT, versus overlap-adding into `((slice x time) x frequency)`: https://gitlab.com/sevagh/xumx_slicq_extra/-/tree/main/umx_experiments/umx-sliCQ-conv3d-orig-branch
* Different sliCQ parameters per target: https://gitlab.com/sevagh/xumx_slicq_extra/-/tree/main/umx_experiments/umx-sliCQ-lstm-branch, https://gitlab.com/sevagh/xumx_slicq_extra/-/tree/main/umx_experiments/umx-sliCQ-first-submission - this would also be compatible with doing the Wiener-EM with the STFT (since 4 different sliCQT cannot have the iterative EM applied to them - also, crossnet loss won't work with 4 different sliCQT)
* LSTM instead of convolutions: https://gitlab.com/sevagh/xumx_slicq_extra/-/tree/main/umx_experiments/umx-sliCQ-lstm-branch
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# 📎 Important info
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* 💪  Challenge Page: https://www.aicrowd.com/challenges/music-demixing-challenge-ismir-2021
* 🗣️  Discussion Forum: https://www.aicrowd.com/challenges/music-demixing-challenge-ismir-2021/discussion
* 🏆  Leaderboard: https://www.aicrowd.com/challenges/music-demixing-challenge-ismir-2021/leaderboards
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Contributors
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- [Stefan Uhlich](https://www.aicrowd.com/participants/StefanUhlich)
- [Fabian-Robert Stöter](https://www.aicrowd.com/participants/faroit)
- [Shivam Khandelwal](https://www.aicrowd.com/participants/shivam)