Dynamic non-negative models for audio source separation

Paris Smaragdis, Gautham Mysore, Nasser Mohammadiha

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

As seen so far, non-negative models can be quite powerful when it comes to resolving mixtures of sounds. However, in such models we often ignore temporal 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 that learns fixed temporal features, a hidden Markov model that learns state transitions and can incorporate language information, and finally a continuous dynamical model that learns how sounds evolve over time and is able to resolve cases where static information is not enough.

Original languageEnglish (US)
Title of host publicationSignals and Communication Technology
PublisherSpringer
Pages49-71
Number of pages23
DOIs
StatePublished - Jan 1 2018

Publication series

NameSignals and Communication Technology
ISSN (Print)1860-4862
ISSN (Electronic)1860-4870

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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  • Cite this

    Smaragdis, P., Mysore, G., & Mohammadiha, N. (2018). Dynamic non-negative models for audio source separation. In Signals and Communication Technology (pp. 49-71). (Signals and Communication Technology). Springer. https://doi.org/10.1007/978-3-319-73031-8_3