Szczegóły publikacji
Opis bibliograficzny
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
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 141925 |
|---|---|
| Data dodania do BaDAP | 2022-09-06 |
| Tekst źródłowy | URL |
| DOI | 10.1109/LSP.2022.3203665 |
| Rok publikacji | 2022 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Czasopismo/seria | IEEE Signal Processing Letters |
Abstract
In this letter, we address the problem of simultaneous separation and dereverberation of overlapped speech recorded in reverberant conditions. The majority of state-of-theart techniques are tailored for either of the two problems, which for the joint task leads to sub-optimum performance or solutions which involve subsequent, cascade processing. In contrast, we propose a jointly optimum approach in which we formulate a single optimization criterion that minimizes variance of undesired signal components at the output of a convolutional filter, weighted with the desired speech variance, subject to a constraint which allows to control the amount of distortions in the estimated speech signal. We then derive a closed-form solution of the proposed convolutional weighted parametric multichannel Wiener (CWPMW) filter which integrates linear-prediction based dereverberation and speech-distortion weighted Wiener filtering in a jointly optimum manner. The results of experiments performed using measured and simulated data indicate superior performance of the proposed approach in comparison with state-of-the-art, which includes sub-optimum cascades of optimum filters for individual tasks, as well as the recently presented jointly optimum, weighted\ power minimization distortionless response (WPD) beamformer.
