Comparative study of example-guided audio source separation approaches based on nonnegative matrix factorization



Comparative study of example-guided audio source separation approaches based on nonnegative matrix factorization

Comparative study of example-guided audio source separation approaches based on nonnegative matrix factorization
Research Paper / MLSP - IEEE International Workshop on Machine Learning for Signal Processing / Sep 2017 / Audio Processing, Machine/Deep Learning/AI

We consider example-guided audio source separation approaches, where the audio mixture to be separated is supplied with source examples that are assumed matching the sources in the mixture both in frequency and time. These approaches were successfully applied to the tasks such as source separation by humming, score-informed music source separation, and music source separation guided by covers. Most of proposed methods are based on nonnegative matrix factorization (NMF) and its variants, including methods using NMF models pre-trained from examples as an initialization of mixture NMF decomposition, methods using those models as hyperparameters of priors of mixture NMF decomposition, and methods using coupled NMF models. Moreover, those methods differ by the choice of the NMF divergence and the NMF prior. However, there is no systematic comparison of all these methods. In this work, we compare existing methods and some new variants on the score-informed and cover-guided source separation tasks.