Titlebook: Audio Source Separation; Shoji Makino Book 2018 Springer International Publishing AG 2018 audio source separation methods.non-negative mat

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Carl C. Gaither,Alma E. Cavazos-Gaither 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 cons

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https://doi.org/10.1007/978-0-387-49577-4ral information, instead focusing on resolving each incoming spectrum independently. In this chapter we will present some methods that learn to incorporate the temporal aspects of sounds and use that information to perform improved separation. We will show three such models, a conlvolutive model tha

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https://doi.org/10.1007/978-0-387-49577-4ensions are introduced within a more general local Gaussian modeling (LGM) framework. These methods are very attractive since allow combining spatial and spectral cues in a joint and principal way, but also are natural extensions and generalizations of many single-channel NMF-based methods to the mu

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Carl C. Gaither,Alma E. Cavazos-Gaithers (IVA) and nonnegative matrix factorization (NMF). IVA is a state-of-the-art technique that utilizes the statistical independence between source vectors. However, since the source model in IVA is based on a spherically symmetric multivariate distribution, IVA cannot utilize the characteristics of s

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Carl C. Gaither,Alma E. Cavazos-Gaither More computationally demanding approaches tend to produce better results, but often not fast enough to be deployed in practical systems. For example, as opposed to the iterative separation algorithms using source-specific dictionaries, a Deep Neural Network (DNN) performs separation via an iteratio

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