Generalized Multichannel Variational Autoencoder For Underdetermined Source Separation

Abstract

This paper deals with a multichannel audio source separation problem under underdetermined conditions. Multichannel Non-negative Matrix Factorization (MNMF) is one of powerful approaches, which adopts the NMF concept for source power spectrogram modeling. This concept is also employed in Independent Low-Rank Matrix Analysis (ILRMA), a special class of the MNMF framework formulated under determined conditions. While these methods work reasonably well for particular types of sound sources, one limitation is that they can fail to work for sources with spectrograms that do not comply with the NMF model. To address this limitation, an extension of ILRMA called the Multichannel Variational Autoencoder (MVAE) method was recently proposed, where a Conditional VAE (CVAE) is used instead of the NMF model for source power spectrogram modeling. This approach has shown to perform impressively in determined source separation tasks thanks to the representation power of DNNs. While the original MVAE m

Stats

• citations10
• S2 citations—
• github stars0
• HF likes0
• heat score7.81
• arxiv keyseki2018generalized

Related papers

• Fast MVAE: Joint Separation And Classification Of Mixed Sources Based On Multichannel Variational Autoencoder With Auxiliary Classifier (2018)10.07
• Fastmvae2: On Improving And Accelerating The Fast Variational Autoencoder-based Source Separation Algorithm For Determined Mixtures (2021)7.81
• Semi-supervised Multichannel Speech Enhancement With Variational Autoencoders And Non-negative Matrix Factorization (2018)12.25
• End-to-end Non-negative Autoencoders For Sound Source Separation (2019)2.26
• Deep Variational Generative Models For Audio-visual Speech Separation (2020)0.00
• Determined Multichannel Blind Source Separation With Clustered Source Model (2024)0.00
• Multichannel Audio Source Separation With Independent Deeply Learned Matrix Analysis Using Product Of Source Models (2021)0.00
• Multichannel Blind Speech Source Separation With A Disjoint Constraint Source Model (2024)0.00