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Consistent Anisotropic Wiener Filtering for Audio Source Separation

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publicationIEEE Workshop on Applications of Signal Processing to Audio and Acoustics
Pages269-273
Number of pages5
DOIs
Publication statusPublished - 2017
Publication typeA4 Article in a conference publication
EventIEEE Workshop on Applications of Signal Processing to Audio and Acoustics -
Duration: 1 Jan 1900 → …

Publication series

NameIEEE Workshop on Applications of Signal Processing to Audio and Acoustics
ISSN (Print)1947-1629

Conference

ConferenceIEEE Workshop on Applications of Signal Processing to Audio and Acoustics
Period1/01/00 → …

Abstract

For audio source separation applications, it is common to apply a Wiener-like filtering to a time-frequency (TF) representation of the data, such as the short-Time Fourier transform (STFT). This approach, in which the phase of the original mixture is assigned to each component, is limited when sources overlap in the TF domain. In this paper, we propose to improve this technique by accounting for two properties of the phase. First, we model the sources by anisotropic Gaussian variables: This model accounts for the non-uniformity of the phase, and permits us to incorporate some prior information about the phase that originates from a sinusoidal model. Second, we exploit the STFT consistency, which is the relationship between STFT coefficients that is due to the redundancy of the STFT. We derive a conjugate gradient algorithm for estimating the corresponding filter, which we refer to as the consistent anisotropic Wiener filter. Experiments conducted on music pieces show that the proposed approach yields results similar to or better than the state-of-The-Art with a dramatic reduction of the computation time.

Publication forum classification

Field of science, Statistics Finland