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Auditory filter-bank compression improves estimation of signal-to-noise ratio for speech in noise

Liu, F; Demosthenous, A; Yasin, I; (2020) Auditory filter-bank compression improves estimation of signal-to-noise ratio for speech in noise. Journal of the Acoustical Society of America , 147 (5) , Article 3197. 10.1121/10.0001168. Green open access

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Abstract

Signal-to-noise ratio (SNR) estimation is necessary for many speech processing applications often challenged by nonstationary noise. The authors have previously demonstrated that the variance of spectral entropy (VSE) is a reliable estimate of SNR in nonstationary noise. Based on pre-estimated VSE-SNR relationship functions, the SNR of unseen acoustic environments can be estimated from the measured VSE. This study predicts that introducing a compressive function based on cochlear processing will increase the stability of the pre-estimated VSE-SNR relationship functions. This study demonstrates that calculating the VSE based on a nonlinear filter-bank, simulating cochlear compression, reduces the VSE-based SNR estimation errors. VSE-SNR relationship functions were estimated using speech tokens presented in babble noise comprised of different numbers of speakers. Results showed that the coefficient of determination (R2) of the estimated VSE-SNR relationship functions have absolute percentage improvements of over 26% when using a filter-bank with a compressive function, compared to when using a linear filter-bank without compression. In 2-talker babble noise, the estimation accuracy is more than 3 dB better than other published methods.

Type: Article
Title: Auditory filter-bank compression improves estimation of signal-to-noise ratio for speech in noise
Open access status: An open access version is available from UCL Discovery
DOI: 10.1121/10.0001168
Publisher version: https://doi.org/10.1121/10.0001168
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Signal processing, Speech communication, Nonlinear filter, MATLAB, Auditory system, Electronic noise, Acoustic ecology, Speech processing systems, Regression analysis, Acoustic filters
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10096546
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