Signal compaction using polynomial EVD for spherical array processing with applications
Signal compaction using polynomial EVD for spherical array processing with applications
Multi-channel signals captured by spatially separated sensors often contain a high level of data redundancy. A compact signal representation enables more efficient storage and processing, which has been exploited for data compression, noise reduction, and speech and image coding. This paper focuses on the compact representation of speech signals acquired by spherical microphone arrays. A polynomial matrix eigenvalue decomposition (PEVD) can spatially decorrelate signals over a range of time lags and is known to achieve optimum multi-channel data compaction. However, the complexity of PEVD algorithms scales at best cubically with the number of channel signals, e.g., the number of microphones comprised in a spherical array used for processing. In contrast, the spherical harmonic transform (SHT) provides a compact spatial representation of the 3-dimensional sound field measured by spherical microphone arrays, referred to as eigenbeam signals, at a cost that rises only quadratically with the number of microphones. Yet, the SHT's spatially orthogonal basis functions cannot completely decorrelate sound field components over a range of time lags. In this work, we propose to exploit the compact representation offered by the SHT to reduce the number of channels used for subsequent PEVD processing. In the proposed framework for signal representation, we show that the diagonality factor improves by up to 7 dB over the microphone signal representation with a significantly lower computation cost. Moreover, when applying this framework to speech enhancement and source separation, the proposed method improves metrics known as short-time objective intelligibility (STOI) and source-to-distortion ratio (SDR) by up to 0.2 and 20 dB, respectively.
Array signal processing, Data compaction, Decorrelation, Matrix decomposition, Microphone arrays, polynomial matrix eigenvalue decomposition, Sensor arrays, Signal representation, source separation, Speech enhancement, speech enhancement, spherical harmonics
3537-3549
Neo, Vincent W.
7ec5cc5f-8248-40ec-8864-b31335d4ddf2
Evers, Christine
93090c84-e984-4cc3-9363-fbf3f3639c4b
Weiss, Stephan
a89960cd-f869-4728-8f8e-c0b60f04f911
Naylor, Patrick A.
13079486-664a-414c-a1a2-01a30bf0997b
Neo, Vincent W.
7ec5cc5f-8248-40ec-8864-b31335d4ddf2
Evers, Christine
93090c84-e984-4cc3-9363-fbf3f3639c4b
Weiss, Stephan
a89960cd-f869-4728-8f8e-c0b60f04f911
Naylor, Patrick A.
13079486-664a-414c-a1a2-01a30bf0997b
Neo, Vincent W., Evers, Christine, Weiss, Stephan and Naylor, Patrick A.
(2023)
Signal compaction using polynomial EVD for spherical array processing with applications.
IEEE/ACM Transactions on Audio Speech and Language Processing, 31, .
(doi:10.1109/TASLP.2023.3313441).
Abstract
Multi-channel signals captured by spatially separated sensors often contain a high level of data redundancy. A compact signal representation enables more efficient storage and processing, which has been exploited for data compression, noise reduction, and speech and image coding. This paper focuses on the compact representation of speech signals acquired by spherical microphone arrays. A polynomial matrix eigenvalue decomposition (PEVD) can spatially decorrelate signals over a range of time lags and is known to achieve optimum multi-channel data compaction. However, the complexity of PEVD algorithms scales at best cubically with the number of channel signals, e.g., the number of microphones comprised in a spherical array used for processing. In contrast, the spherical harmonic transform (SHT) provides a compact spatial representation of the 3-dimensional sound field measured by spherical microphone arrays, referred to as eigenbeam signals, at a cost that rises only quadratically with the number of microphones. Yet, the SHT's spatially orthogonal basis functions cannot completely decorrelate sound field components over a range of time lags. In this work, we propose to exploit the compact representation offered by the SHT to reduce the number of channels used for subsequent PEVD processing. In the proposed framework for signal representation, we show that the diagonality factor improves by up to 7 dB over the microphone signal representation with a significantly lower computation cost. Moreover, when applying this framework to speech enhancement and source separation, the proposed method improves metrics known as short-time objective intelligibility (STOI) and source-to-distortion ratio (SDR) by up to 0.2 and 20 dB, respectively.
More information
e-pub ahead of print date: 8 September 2023
Keywords:
Array signal processing, Data compaction, Decorrelation, Matrix decomposition, Microphone arrays, polynomial matrix eigenvalue decomposition, Sensor arrays, Signal representation, source separation, Speech enhancement, speech enhancement, spherical harmonics
Identifiers
Local EPrints ID: 483631
URI: http://eprints.soton.ac.uk/id/eprint/483631
ISSN: 2329-9290
PURE UUID: 0febbd30-7fc3-40ad-af1d-d0929a7f2a0b
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Date deposited: 02 Nov 2023 17:58
Last modified: 18 Mar 2024 03:56
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Contributors
Author:
Vincent W. Neo
Author:
Christine Evers
Author:
Stephan Weiss
Author:
Patrick A. Naylor
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