Source Separation of Multi-source Raw Music using a Residual Quantized Variational Autoencoder
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911986715983872 |
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| author | Berti, Leonardo |
| author_facet | Berti, Leonardo |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_07020 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Source Separation of Multi-source Raw Music using a Residual Quantized Variational Autoencoder Berti, Leonardo Sound Machine Learning Multimedia Audio and Speech Processing 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 |
| title | Source Separation of Multi-source Raw Music using a Residual Quantized Variational Autoencoder |
| topic | Sound Machine Learning Multimedia Audio and Speech Processing |
| url | https://arxiv.org/abs/2408.07020 |