Szczegóły publikacji

Opis bibliograficzny

Combating reverberation in NTF-based speech separation using a sub-source weighted multichannel Wiener filter and linear prediction / Mieszko FRAŚ, Marcin WITKOWSKI, Konrad KOWALCZYK // W: INTERSPEECH 2021 [Dokument elektroniczny] : proceedings of the 22nd annual conference of the International Speech Communication Association : 30 August–3 September 2021, Brno, Czechia. — Brno : Brno University of Technology, 2021. — (Interspeech : Proceedings of the ... Annual Conference of the International Speech Communication Association ; ISSN 1990-9772). — e-ISBN: 978-171383690-2. — S. 3895–3899. — Wymagania systemowe: Adobe Reader. — Tryb dostępu: https://www.isca-speech.org/archive/pdfs/interspeech_2021/fra... [2021-09-21]. — Bibliogr. s. 3899. Abstr. — W bazie Scopus zakres stron: 2403-2407

Autorzy (3)

Słowa kluczowe

multi-channel Wiener filtersource separationsubsource modellingdereverberationnon negative tensor factorizationweighted prediction error

Dane bibliometryczne

ID BaDAP136293
Data dodania do BaDAP2021-09-27
DOI10.21437/Interspeech.2021-1230
Rok publikacji2021
Typ publikacjimateriały konferencyjne (aut.)
Otwarty dostęptak
KonferencjaInterspeech 2021
Czasopismo/seriaInterspeech

Abstract

Sound source separation (SS) from the microphone signals capturing speech in reverberant conditions is a formidable task. This paper addresses the problem of joint separation and dereverberation of speech using the multichannel Wiener filter (MWF) that is tailored to the sub-source modeling of each speech source with a full-rank mixing matrix. Specifically, the parameters of the proposed sub-source-weighted (SSW) spatial filter are estimated using the sub-source based expectation maximization (EM) algorithm with multiplicative updates (MU) and the localization prior distribution (LP) on the mixing matrix (SSEM-MU-LP). In addition, we strengthen dereverberation by incorporating a Generalized Weighted Prediction Error (GWPE) algorithm. The proposed method is evaluated using a large dataset of two-channel recordings of clean speech convolved with both real and synthesized impulse responses. The results of the experiments show the superior performance of the proposed method in reverberant conditions in comparison to using the standard NTF-based separation with the vanilla MWF in terms of signal-to-distortion ratio (improvement of 3–5.6 dB) and other commonly used sound separation metrics.

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#140049Data dodania: 17.5.2022
Convolutional weighted minimum mean square error filter for joint source separation and dereverberation / Mieszko FRAŚ, Marcin WITKOWSKI, Konrad KOWALCZYK // W: ICASSP 2022 [Dokument elektroniczny] : 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing : 7–13 May 2022, virtual, 22–27 May 2022, Singapore, satellite venue: Shenzhen, China : proceedings. — Wersja do Windows. — Dane tekstowe. — Piscataway : The Institute of Electrical and Electronics Engineers, cop. 2022. — ( Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing ; ISSN  1520-6149 ). — e-ISBN: 978-1-6654-0540-9. — S. 286–290. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 290, Abstr. — Publikacja dostępna online od: 2022-04-27
artykuł
#141925Data dodania: 6.9.2022
Convolutional weighted parametric multichannel Wiener filter for reverberant source separation / Mieszko FRAŚ, Konrad KOWALCZYK // IEEE Signal Processing Letters ; ISSN 1070-9908. — 2022 — vol. 29, s. 1928–1932. — Bibliogr. s. 1932, Abstr. — Publikacja dostępna online od: 2022-09-01