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Probabilistic Modelling of Signal Mixtures with Differentiable Dictionaries

arXiv 2211.15439 ↗First posted 28 Nov 2022eess.AS cs.LG cs.SDcited by 0

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Abstract (arXiv)

We introduce a novel way to incorporate prior information into (semi-) supervised non-negative matrix factorization, which we call differentiable dictionary search. It enables general, highly flexible and principled modelling of mixtures where non-linear sources are linearly mixed. We study its behavior on an audio decomposition task, and conduct an extensive, highly controlled study of its modelling capabilities.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 16 references that are in the map. First posted on arXiv 28 Nov 2022.

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References · 6

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2016Density estimation using Real NVPcited by 2,029
  3. 2017Self-Normalizing Neural Networkscited by 591
  4. 2017Statistical Speech Enhancement Based on Probabilistic Integration of Variational Autoencoder and Non-Negative Matrix Factorizationcited by 33
  5. 2017Generative Adversarial Source Separationcited by 19
  6. 2017Adversarial Semi-Supervised Audio Source Separation applied to Singing Voice Extractioncited by 11

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