Source Separation of Multi-source Raw Music using a Residual Quantized Variational Autoencoder
<|reference_start|>Source Separation of Multi-source Raw Music using a Residual Quantized Variational Autoencoder: I developed a neural audio codec model based on the residual quantized variational autoencoder architecture. I train the model on the Slakh2100 dataset, a standard dataset for musical source separation, composed of multi-track audio. The model can separate audio sources, achieving almost SoTA results with much less computing power. The code is publicly available at github.com/LeonardoBerti00/Source-Separation-of-Multi-source-Music-using-Residual-Quantizad-Variational-Autoencoder<|reference_end|>
@article{berti2024source,
    title={Source Separation of Multi-source Raw Music using a Residual Quantized
  Variational Autoencoder},
    author={Leonardo Berti},
    journal={arXiv preprint arXiv:2408.07020},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.07020},
    primaryClass={cs.SD cs.LG cs.MM eess.AS}
}