SEDA: A tunable Q-factor wavelet-based noise reduction algorithm for multi-talker babble

Roozbeh Soleymani; Ivan W. Selesnick; David M. Landsberger

doi:10.1016/j.specom.2017.11.004

SEDA: A tunable Q-factor wavelet-based noise reduction algorithm for multi-talker babble

Roozbeh Soleymani, Ivan W. Selesnick, David M. Landsberger

Electrical and Computer Engineering

Research output: Contribution to journal › Article › peer-review

Abstract

We introduce a new wavelet-based algorithm to enhance the quality of speech corrupted by multi-talker babble noise. The algorithm comprises three stages: The first stage classifies short frames of the noisy speech as speech-dominated or noise-dominated. We design this classifier specifically for multi-talker babble noise. The second stage performs preliminary de-nosing of noisy speech frames using oversampled wavelet transforms and parallel group thresholding. The final stage performs further denoising by attenuating residual high frequency components in the signal produced by the second stage. A significant improvement in intelligibility and quality was observed in evaluation tests of the algorithm with cochlear implant users.

Original language	English (US)
Pages (from-to)	102-115
Number of pages	14
Journal	Speech Communication
Volume	96
DOIs	https://doi.org/10.1016/j.specom.2017.11.004
State	Published - Feb 2018

ASJC Scopus subject areas

Software
Modeling and Simulation
Communication
Language and Linguistics
Linguistics and Language
Computer Vision and Pattern Recognition
Computer Science Applications

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10.1016/j.specom.2017.11.004

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title = "SEDA: A tunable Q-factor wavelet-based noise reduction algorithm for multi-talker babble",

abstract = "We introduce a new wavelet-based algorithm to enhance the quality of speech corrupted by multi-talker babble noise. The algorithm comprises three stages: The first stage classifies short frames of the noisy speech as speech-dominated or noise-dominated. We design this classifier specifically for multi-talker babble noise. The second stage performs preliminary de-nosing of noisy speech frames using oversampled wavelet transforms and parallel group thresholding. The final stage performs further denoising by attenuating residual high frequency components in the signal produced by the second stage. A significant improvement in intelligibility and quality was observed in evaluation tests of the algorithm with cochlear implant users.",

author = "Roozbeh Soleymani and Selesnick, {Ivan W.} and Landsberger, {David M.}",

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doi = "10.1016/j.specom.2017.11.004",

language = "English (US)",

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AU - Landsberger, David M.

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N2 - We introduce a new wavelet-based algorithm to enhance the quality of speech corrupted by multi-talker babble noise. The algorithm comprises three stages: The first stage classifies short frames of the noisy speech as speech-dominated or noise-dominated. We design this classifier specifically for multi-talker babble noise. The second stage performs preliminary de-nosing of noisy speech frames using oversampled wavelet transforms and parallel group thresholding. The final stage performs further denoising by attenuating residual high frequency components in the signal produced by the second stage. A significant improvement in intelligibility and quality was observed in evaluation tests of the algorithm with cochlear implant users.

AB - We introduce a new wavelet-based algorithm to enhance the quality of speech corrupted by multi-talker babble noise. The algorithm comprises three stages: The first stage classifies short frames of the noisy speech as speech-dominated or noise-dominated. We design this classifier specifically for multi-talker babble noise. The second stage performs preliminary de-nosing of noisy speech frames using oversampled wavelet transforms and parallel group thresholding. The final stage performs further denoising by attenuating residual high frequency components in the signal produced by the second stage. A significant improvement in intelligibility and quality was observed in evaluation tests of the algorithm with cochlear implant users.

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JO - Speech Communication

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SEDA: A tunable Q-factor wavelet-based noise reduction algorithm for multi-talker babble

Abstract

ASJC Scopus subject areas

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