Szczegóły publikacji

Opis bibliograficzny

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


Autorzy (3)


Słowa kluczowe

Wiener filterweighted prediction errorminimum mean square errordereverberationsource separation

Dane bibliometryczne

ID BaDAP140049
Data dodania do BaDAP2022-05-17
Tekst źródłowyURL
DOI10.1109/ICASSP43922.2022.9746581
Rok publikacji2022
Typ publikacjimateriały konferencyjne (aut.)
Otwarty dostęptak
WydawcaInstitute of Electrical and Electronics Engineers (IEEE)
Konferencja2022 IEEE International Conference on Acoustics, Speech and Signal Processing
Czasopismo/seriaProceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing

Abstract

Practical scenarios with multiple simultaneously active speakers recorded using one or more microphones in reverberant rooms pose a challenging problem when the extraction of the desired speaker signal is sought for. The majority of techniques found in the literature facilitate either source separation or dereverberation, which can at best be performed as subsequent, cascade processing. Recently, a solution to the joint task has been proposed, which is known as the weighted power minimization distortionless response (WPD) beamformer. In this paper, we derive a convolutional multichannel filter which performs jointly optimum dereverberation and desired source signal extraction. We formulate a single optimization criterion which minimizes the convolutional source-variance weighted mean square error (CW-MMSE), thereby effectively unifying the weighted prediction error (WPE) based dereverberation and MMSE filtering for the desired source extraction from reverberant mixtures of speakers. Experimental results show a significant performance improvement over the compared state-of-the-art methods such as WPD for datasets with simulated and recorded impulse responses.

Publikacje, które mogą Cię zainteresować

artykuł
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
fragment książki
Wishart localization prior on spatial covariance matrix in ambisonic source separation using non-negative tensor factorization / Mateusz GUZIK, 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. 446–450. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 450, Abstr. — Publikacja dostępna online od: 2022-04-27