Name
Abstract | ||
|---|---|---|
We previously proposed an optimal (in the maximum likelihood sense) convolutional beamformer that can perform simultaneous denoising and dereverberation, and showed its superiority over the widely used cascade of a Weighted Prediction Error (WPE) dereverberation filter and a conventional Minimum-Power Distortionless Response (MPDR) beamformer. However, it has not been fully investigated which components in the convolutional beamformer yield such superiority. To this end, this paper presents a new derivation of the convolutional beamformer that allows us to factorize it into a WPE dereverberation filter, and a special type of a (non-convolutional) beamformer, referred to as a weighted MPDR (wM-PDR) beamformer, without loss of optimality. With experiments, we show that the superiority of the convolutional beamformer in fact comes from its wMPDR part. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICASSP40776.2020.9054393 | ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
Keywords | DocType | ISSN |
Dereverberation,beamforming,speech enhancement | Conference | 1520-6149 |
ISBN | Citations | PageRank |
978-1-5090-6632-2 | 1 | 0.35 |
References | Authors | |
10 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Boeddeker Christoph | 1 | 3 | 3.84 |
| Tomohiro Nakatani | 2 | 1327 | 139.18 |
| Keisuke Kinoshita | 3 | 494 | 54.81 |
| Reinhold Haeb-Umbach | 4 | 1487 | 211.71 |