説明
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 WPE dereverberation filter and a conventional 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 wMPDR beamformer, without loss of optimality. With experiments, we show that the superiority of the convolutional beamformer in fact comes from its wMPDR part.
Submitted to ICASSP 2020
収録刊行物
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- ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 216-220, 2020-05-01
IEEE
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キーワード
- FOS: Computer and information sciences
- Sound (cs.SD)
- Computer Science - Computation and Language
- Computer Science - Sound
- Audio and Speech Processing (eess.AS)
- FOS: Electrical engineering, electronic engineering, information engineering
- Computation and Language (cs.CL)
- Electrical Engineering and Systems Science - Audio and Speech Processing
詳細情報 詳細情報について
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- CRID
- 1872553967994306944
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- データソース種別
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- OpenAIRE