Separation of known sources using non-negative spectrogram factorisation

Abstract

This chapter presents non-negative spectrogram factorisation (NMF) techniques which can be used to separate sources in the cases where source-specific training material is available in advance. We first present the basic NMF formulation for sound mixtures and then present criteria and algorithms for estimating the model parameters. We introduce selected methods for training the NMF source models by using either vector quantisation, convexity constraints, archetypal analysis, or discriminative methods. We also explain how the learned dictionaries can be adapted to deal with mismatches between the training data and usage scenario. We present also how semi-supervised learning can be used to deal with unknown noise sources within a mixture and finally we introduce a coupled NMF method which can be used to model large temporal context while retaining low algorithmic latency.