A Controlled Noise Reduction Wiener Filter Based on the Quadratic Eigenvalue Problem

Ningning Pan; Jacob Benesty; Jingdong Chen

doi:10.1109/APSIPAASC58517.2023.10317468

A Controlled Noise Reduction Wiener Filter Based on the Quadratic Eigenvalue Problem

Ningning Pan, Jacob Benesty, Jingdong Chen

School of Marine Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

This paper deals with the problem of multichannel noise reduction and a controlled Wiener filter is developed in the frequency domain by minimizing the subband mean-squared error between the clean speech of interest and its estimate subject to a constraint on the residual noise level. Using the Lagrange multiplier, we then transform the constrained minimization problem into one of quadratic eigenvalue problem. Different forms of the controlled Wiener filter are then derived. Depending on how the quadratic eigenvalues are used, these filters can control the amount of noise reduction as well as the compromise between the amount of noise reduction and the level of speech distortion. Simulation results justify the properties of the controlled Wiener filter.

Original language	English
Title of host publication	2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1990-1994
Number of pages	5
ISBN (Electronic)	9798350300673
DOIs	https://doi.org/10.1109/APSIPAASC58517.2023.10317468
State	Published - 2023
Event	2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023 - Taipei, Taiwan, Province of China Duration: 31 Oct 2023 → 3 Nov 2023

Publication series

Name	2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023

Conference

Conference	2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023
Country/Territory	Taiwan, Province of China
City	Taipei
Period	31/10/23 → 3/11/23

Access to Document

10.1109/APSIPAASC58517.2023.10317468

Cite this

Pan, N., Benesty, J., & Chen, J. (2023). A Controlled Noise Reduction Wiener Filter Based on the Quadratic Eigenvalue Problem. In 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023 (pp. 1990-1994). (2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/APSIPAASC58517.2023.10317468

Pan, Ningning ; Benesty, Jacob ; Chen, Jingdong. / A Controlled Noise Reduction Wiener Filter Based on the Quadratic Eigenvalue Problem. 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 1990-1994 (2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023).

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title = "A Controlled Noise Reduction Wiener Filter Based on the Quadratic Eigenvalue Problem",

abstract = "This paper deals with the problem of multichannel noise reduction and a controlled Wiener filter is developed in the frequency domain by minimizing the subband mean-squared error between the clean speech of interest and its estimate subject to a constraint on the residual noise level. Using the Lagrange multiplier, we then transform the constrained minimization problem into one of quadratic eigenvalue problem. Different forms of the controlled Wiener filter are then derived. Depending on how the quadratic eigenvalues are used, these filters can control the amount of noise reduction as well as the compromise between the amount of noise reduction and the level of speech distortion. Simulation results justify the properties of the controlled Wiener filter.",

author = "Ningning Pan and Jacob Benesty and Jingdong Chen",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023 ; Conference date: 31-10-2023 Through 03-11-2023",

year = "2023",

doi = "10.1109/APSIPAASC58517.2023.10317468",

language = "英语",

series = "2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1990--1994",

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Pan, N, Benesty, J & Chen, J 2023, A Controlled Noise Reduction Wiener Filter Based on the Quadratic Eigenvalue Problem. in 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023. 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023, Institute of Electrical and Electronics Engineers Inc., pp. 1990-1994, 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023, Taipei, Taiwan, Province of China, 31/10/23. https://doi.org/10.1109/APSIPAASC58517.2023.10317468

A Controlled Noise Reduction Wiener Filter Based on the Quadratic Eigenvalue Problem. / Pan, Ningning; Benesty, Jacob; Chen, Jingdong.
2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 1990-1994 (2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - This paper deals with the problem of multichannel noise reduction and a controlled Wiener filter is developed in the frequency domain by minimizing the subband mean-squared error between the clean speech of interest and its estimate subject to a constraint on the residual noise level. Using the Lagrange multiplier, we then transform the constrained minimization problem into one of quadratic eigenvalue problem. Different forms of the controlled Wiener filter are then derived. Depending on how the quadratic eigenvalues are used, these filters can control the amount of noise reduction as well as the compromise between the amount of noise reduction and the level of speech distortion. Simulation results justify the properties of the controlled Wiener filter.

AB - This paper deals with the problem of multichannel noise reduction and a controlled Wiener filter is developed in the frequency domain by minimizing the subband mean-squared error between the clean speech of interest and its estimate subject to a constraint on the residual noise level. Using the Lagrange multiplier, we then transform the constrained minimization problem into one of quadratic eigenvalue problem. Different forms of the controlled Wiener filter are then derived. Depending on how the quadratic eigenvalues are used, these filters can control the amount of noise reduction as well as the compromise between the amount of noise reduction and the level of speech distortion. Simulation results justify the properties of the controlled Wiener filter.

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Pan N, Benesty J, Chen J. A Controlled Noise Reduction Wiener Filter Based on the Quadratic Eigenvalue Problem. In 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 1990-1994. (2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023). doi: 10.1109/APSIPAASC58517.2023.10317468

A Controlled Noise Reduction Wiener Filter Based on the Quadratic Eigenvalue Problem

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