Blind and Neural Network-Guided Convolutional Beamformer for Joint Denoising, Dereverberation, and Source Separation Article Swipe
Tomohiro Nakatani
,
Rintaro Ikeshita
,
Keisuke Kinoshita
,
Hiroshi Sawada
,
Shoko Araki
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/icassp39728.2021.9414264
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/icassp39728.2021.9414264
This paper proposes an approach for optimizing a Convolutional BeamFormer (CBF) that can jointly perform denoising (DN), dereverberation (DR), and source separation (SS). First, we develop a blind CBF optimization algorithm that requires no prior information on the sources or the room acoustics, by extending a conventional joint DR and SS method. For making the optimization computationally tractable, we incorporate two techniques into the approach: the Source-Wise Factorization (SW-Fact) of a CBF and the Independent Vector Extraction (IVE). To further improve the performance, we develop a method that integrates a neural network(NN) based source power spectra estimation with CBF optimization by an inverse-Gamma prior. Experiments using noisy reverberant mixtures reveal that our proposed method with both blind and NN-guided scenarios greatly outperforms the conventional state-of-the-art NN-supported mask-based CBF in terms of the improvement in automatic speech recognition and signal distortion reduction performance.
Related Topics
Concepts
Computer science
Blind signal separation
Noise reduction
Source separation
Convolutional neural network
Speech enhancement
Reduction (mathematics)
Distortion (music)
Joint (building)
Speech recognition
Artificial intelligence
Pattern recognition (psychology)
Noise (video)
Algorithm
Mathematics
Engineering
Computer network
Architectural engineering
Amplifier
Image (mathematics)
Channel (broadcasting)
Bandwidth (computing)
Geometry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/icassp39728.2021.9414264
- OA Status
- green
- Cited By
- 22
- References
- 43
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3163391314
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3163391314Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/icassp39728.2021.9414264Digital Object Identifier
- Title
-
Blind and Neural Network-Guided Convolutional Beamformer for Joint Denoising, Dereverberation, and Source SeparationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-05-13Full publication date if available
- Authors
-
Tomohiro Nakatani, Rintaro Ikeshita, Keisuke Kinoshita, Hiroshi Sawada, Shoko ArakiList of authors in order
- Landing page
-
https://doi.org/10.1109/icassp39728.2021.9414264Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2108.01836Direct OA link when available
- Concepts
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Computer science, Blind signal separation, Noise reduction, Source separation, Convolutional neural network, Speech enhancement, Reduction (mathematics), Distortion (music), Joint (building), Speech recognition, Artificial intelligence, Pattern recognition (psychology), Noise (video), Algorithm, Mathematics, Engineering, Computer network, Architectural engineering, Amplifier, Image (mathematics), Channel (broadcasting), Bandwidth (computing), GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
22Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 6, 2023: 4, 2022: 6, 2021: 3Per-year citation counts (last 5 years)
- References (count)
-
43Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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