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Source Separation of Multi-source Raw Music using a Residual Quantized Variational Autoencoder

arXiv 2408.07020 ↗First posted 12 Aug 2024cs.SD cs.LG cs.MMcited by 0

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Abstract (arXiv)

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

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 20 of its 25 references that are in the map. First posted on arXiv 12 Aug 2024.

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References · 20

  1. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  2. 2021SoundStream: An End-to-End Neural Audio Codeccited by 756
  3. 2022Autoregressive Image Generation using Residual Quantizationcited by 706
  4. 2015Empirical Evaluation of Rectified Activations in Convolutional Networkcited by 515
  5. 2018SDR - half-baked or well done?cited by 420
  6. 2018Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separationcited by 400
  7. 2019Music Source Separation in the Waveform Domaincited by 98
  8. 2021Hybrid Spectrogram and Waveform Source Separationcited by 90
  9. 2019Cutting Music Source Separation Some Slakh: A Dataset to Study the Impact of Training Data Quality and Quantitycited by 86
  10. 2023Multi-Source Diffusion Models for Simultaneous Music Generation and Separationcited by 63
  11. 2020A Spectral Energy Distance for Parallel Speech Synthesiscited by 42
  12. 2020Source Separation with Deep Generative Priorscited by 35
8 more
  1. 2017Generative Adversarial Source Separationcited by 19
  2. 2018End-to-end music source separation: is it possible in the waveform domain?cited by 19
  3. 2021Parallel and Flexible Sampling from Autoregressive Models via Langevin Dynamicscited by 19
  4. 2022On loss functions and evaluation metrics for music source separationcited by 17
  5. 2022Evaluating generative audio systems and their metricscited by 16
  6. 2020LaSAFT: Latent Source Attentive Frequency Transformation for Conditioned Source Separationcited by 15
  7. 2023Latent Autoregressive Source Separationcited by 12
  8. 2022Improving Source Separation by Explicitly Modeling Dependencies Between Sourcescited by 8

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