A Joint Diagonalization Based Efficient Approach to Underdetermined Blind Audio Source Separation Using the Multichannel Wiener Filter Article Swipe
Nobutaka Ito
,
Rintaro Ikeshita
,
Hiroshi Sawada
,
Tomohiro Nakatani
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2101.08563
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2101.08563
This paper presents a computationally efficient approach to blind source separation (BSS) of audio signals, applicable even when there are more sources than microphones (i.e., the underdetermined case). When there are as many sources as microphones (i.e., the determined case), BSS can be performed computationally efficiently by independent component analysis (ICA). Unfortunately, however, ICA is basically inapplicable to the underdetermined case. Another BSS approach using the multichannel Wiener filter (MWF) is applicable even to this case, and encompasses full-rank spatial covariance analysis (FCA) and multichannel non-negative matrix factorization (MNMF). However, these methods require massive numbers of matrix inversions to design the MWF, and are thus computationally inefficient. To overcome this drawback, we exploit the well-known property of diagonal matrices that matrix inversion amounts to mere inversion of the diagonal elements and can thus be performed computationally efficiently. This makes it possible to drastically reduce the computational cost of the above matrix inversions based on a joint diagonalization (JD) idea, leading to computationally efficient BSS. Specifically, we restrict the N spatial covariance matrices (SCMs) of all N sources to a class of (exactly) jointly diagonalizable matrices. Based on this approach, we present FastFCA, a computationally efficient extension of FCA. We also present a unified framework for underdetermined and determined audio BSS, which highlights a theoretical connection between FastFCA and other methods. Moreover, we reveal that FastFCA can be regarded as a regularized version of approximate joint diagonalization (AJD).
Related Topics
Concepts
Underdetermined system
Blind signal separation
Computer science
Wiener filter
Diagonalizable matrix
Independent component analysis
Algorithm
Matrix decomposition
Inversion (geology)
Theoretical computer science
Artificial intelligence
Symmetric matrix
Eigenvalues and eigenvectors
Telecommunications
Structural basin
Biology
Paleontology
Quantum mechanics
Channel (broadcasting)
Physics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2101.08563
- https://arxiv.org/pdf/2101.08563
- OA Status
- green
- Cited By
- 1
- References
- 60
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3121576124
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3121576124Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2101.08563Digital Object Identifier
- Title
-
A Joint Diagonalization Based Efficient Approach to Underdetermined Blind Audio Source Separation Using the Multichannel Wiener FilterWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-01-21Full publication date if available
- Authors
-
Nobutaka Ito, Rintaro Ikeshita, Hiroshi Sawada, Tomohiro NakataniList of authors in order
- Landing page
-
https://arxiv.org/abs/2101.08563Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2101.08563Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2101.08563Direct OA link when available
- Concepts
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Underdetermined system, Blind signal separation, Computer science, Wiener filter, Diagonalizable matrix, Independent component analysis, Algorithm, Matrix decomposition, Inversion (geology), Theoretical computer science, Artificial intelligence, Symmetric matrix, Eigenvalues and eigenvectors, Telecommunications, Structural basin, Biology, Paleontology, Quantum mechanics, Channel (broadcasting), PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2021: 1Per-year citation counts (last 5 years)
- References (count)
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60Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| primary_location.raw_type | text |
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| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2101.08563 |
| publication_date | 2021-01-21 |
| publication_year | 2021 |
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