Unsupervised Audio Source Separation Using Differentiable Parametric Source Models

by   Kilian Schulze-Forster, et al.
Télécom Paris

Supervised deep learning approaches to underdetermined audio source separation achieve state-of-the-art performance but require a dataset of mixtures along with their corresponding isolated source signals. Such datasets can be extremely costly to obtain for musical mixtures. This raises a need for unsupervised methods. We propose a novel unsupervised model-based deep learning approach to musical source separation. Each source is modelled with a differentiable parametric source-filter model. A neural network is trained to reconstruct the observed mixture as a sum of the sources by estimating the source models' parameters given their fundamental frequencies. At test time, soft masks are obtained from the synthesized source signals. The experimental evaluation on a vocal ensemble separation task shows that the proposed method outperforms learning-free methods based on nonnegative matrix factorization and a supervised deep learning baseline. Integrating domain knowledge in the form of source models into a data-driven method leads to high data efficiency: the proposed approach achieves good separation quality even when trained on less than three minutes of audio. This work makes powerful deep learning based separation usable in scenarios where training data with ground truth is expensive or nonexistent.


page 1

page 4

page 5


Model selection for deep audio source separation via clustering analysis

Audio source separation is the process of separating a mixture (e.g. a p...

Deep Learning Based Source Separation Applied To Choir Ensembles

Choral singing is a widely practiced form of ensemble singing wherein a ...

Multichannel Singing Voice Separation by Deep Neural Network Informed DOA Constrained CNMF

This work addresses the problem of multichannel source separation combin...

Unsupervised Source Separation via Bayesian Inference in the Latent Domain

State of the art audio source separation models rely on supervised data-...

Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model

This paper presents an unsupervised method that trains neural source sep...

Unsupervised Audio Source Separation via Spectrum Energy Preserved Wasserstein Learning

Separating audio mixtures into individual tracks has been a long standin...

Data-Driven Blind Synchronization and Interference Rejection for Digital Communication Signals

We study the potential of data-driven deep learning methods for separati...

Please sign up or login with your details

Forgot password? Click here to reset