TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

Bayesian anisotropic Gaussian model for audio source separation

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
KustantajaIEEE
Sivumäärä5
ISBN (elektroninen)978-1-5386-4657-1
DOI - pysyväislinkit
TilaJulkaistu - huhtikuuta 2018
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE International Conference on Acoustics, Speech and Signal Processing - Calgary, Kanada
Kesto: 15 huhtikuuta 201820 huhtikuuta 2018

Julkaisusarja

Nimi
ISSN (elektroninen)2379-190X

Conference

ConferenceIEEE International Conference on Acoustics, Speech and Signal Processing
MaaKanada
KaupunkiCalgary
Ajanjakso15/04/1820/04/18

Tiivistelmä

In audio source separation applications, it is common to model the sources as circular-symmetric Gaussian random variables, which is equivalent to assuming that the phase of each source is uniformly distributed. In this paper, we introduce an anisotropic Gaussian source model in which both the magnitude and phase parameters are modeled as random variables. In such a model, it becomes possible to promote a phase value that originates from a signal model and to adjust the relative importance of this underlying model-based phase constraint. We conduct Bayesian inference of the model through the derivation of an expectation-maximization algorithm for estimating the parameters. Experiments conducted on realistic music songs for a monaural source separation task, in an scenario where the variance parameters are assumed known, show that the proposed approach outperforms state-of-the-art techniques.

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