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README.md

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+---
+license: cc-by-4.0
+tags:
+- music-source-separation
+- blind-source-separation
+- flow-matching
+- diffusion-transformer
+- audio-queries
+library_name: pytorch
+---
+
+# MuS3D: Music Source Separation via Stem Discovery
+
+Model checkpoints accompanying the paper *"Music Source Separation via Stem Discovery"*. 
+
+
+
+## Contents
+
+Each folder contains the weights and a `config.yaml` describing the architecture and training setup:
+
+- `SEG_mixemb`: SEG, the stem embedding generator. A diffusion transformer (37M parameters) trained with conditional flow matching on PCA-reduced PaSST embeddings; generates stem queries and residual embeddings.
+- `SEG_resemb`: SEG variant additionally conditioned on the residual embedding from the previous iteration.
+- `DiT`: DiT-Sep, the conditional flow matching separator based on SAM-Audio (large, ~2.2B parameters), conditioned on query embeddings and operating on DAC-VAE latents; fine-tuned from the public SAM-Audio weights.
+- `BS-Locoformer_query`: BS-Locoformer (medium band-split TF-Locoformer) with query conditioning via SwiGLU bias modulation.
+- `BS-Locoformer_static`: the same architecture trained as a static 6-stem (VDBGPO) separator, used as a baseline.
+- `classifiers/pca_params_train.pkl`: fitted PCA parameters used to reduce the 1024-dimensional PaSST embeddings to 128 dimensions.
+- `classifiers/stem_classifier_pcatrain.pkl`: hierarchical stem classifier over the PCA-reduced PaSST embeddings, used to assign taxonomy labels to generated queries during evaluation.
+- `classifiers/multilabel_classifier.pkl`: multi-label mixture classifier, the stem-discovery baseline from Table 1 of the paper.
+
+## Code
+
+Code and training details: [github.com/REMUS-Aalto/Mus3D](https://github.com/REMUS-Aalto/Mus3D)
+
+Sound examples: [research.spa.aalto.fi/publications/papers/mus3d](http://research.spa.aalto.fi/publications/papers/mus3d)
+
+## Citation
+
+```bibtex
+@article{kallinen2026mus3d,
+  title   = {Music Source Separation via Stem Discovery},
+  author  = {Kallinen, V. Valtteri and Moliner, Eloi and Juvela, Lauri and V{\"a}lim{\"a}ki, Vesa},
+  journal = {arXiv preprint arXiv:2609.30912},
+  year    = {2026},
+}
+```
+
+## License
+
+The model weights are released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
+
+## Acknowledgments
+
+This research was funded by the Research Council of Finland (grant no. 371845). Computational resources were provided by the Aalto Science-IT project.