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Dual-channel spectral subtraction algorithms based speech enhancement dedicated to a bilateral cochlear implant

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Title: Dual-channel spectral subtraction algorithms based speech enhancement dedicated to a bilateral cochlear implant
Authors: Kallel, Fathi1,2 fathikallel@yahoo.fr, Frikha, Mondher1, Ghorbel, Mohamed1, Hamida, Ahmed Ben1, Berger-Vachon, Christian2
Source: Applied Acoustics. Jan2012, Vol. 73 Issue 1, p12-20. 9p.
Subjects: Cochlear implants, Signal-to-noise ratio, Algorithms, Speech perception, Estimation theory, Spectral energy distribution, Signal processing
Abstract: Abstract: In this paper, two speech enhancement algorithms (SEAs) based on spectral subtraction (SS) principle have been evaluated for bilateral cochlear implant (BCI) users. Specifically, dual-channel noise power spectral estimation algorithm using power spectral densities (PSD) and cross power spectral density (CPSD) of the observed signals was studied. The enhanced speech signals were obtained using either Dual Channel Non Linear Spectral Subtraction ‘DC-NLSS’ or Dual-Channel Multi-Band Spectral Subtraction ‘DC-MBSS’ algorithms. For performance evaluation, some objective speech assessment tests relying on Perceptual Evaluation of Speech Quality (PESQ) score and speech Itakura-Saito (IS) distortion measurement were performed to fix the optimal number of frequency band needed in DC-MBSS algorithm. In order to evaluate the speech intelligibility, subjective listening tests were assessed with 50 normal hearing listeners using a specific BCI simulator and with three deafened BCI patients. Experimental results, obtained using French Lafon database corrupted by an additive babble noise at different Signal-to-Noise Ratios (SNR), showed that DC-MBSS algorithm improves speech understanding better than DC-NLSS algorithm for single and multiple interfering noise sources. [Copyright &y& Elsevier]
Copyright of Applied Acoustics is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 65045946
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: <searchLink fieldCode="DE" term="%22Cochlear+implants%22">Cochlear implants</searchLink><br /><searchLink fieldCode="DE" term="%22Signal-to-noise+ratio%22">Signal-to-noise ratio</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Speech+perception%22">Speech perception</searchLink><br /><searchLink fieldCode="DE" term="%22Estimation+theory%22">Estimation theory</searchLink><br /><searchLink fieldCode="DE" term="%22Spectral+energy+distribution%22">Spectral energy distribution</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+processing%22">Signal processing</searchLink>
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  Data: Abstract: In this paper, two speech enhancement algorithms (SEAs) based on spectral subtraction (SS) principle have been evaluated for bilateral cochlear implant (BCI) users. Specifically, dual-channel noise power spectral estimation algorithm using power spectral densities (PSD) and cross power spectral density (CPSD) of the observed signals was studied. The enhanced speech signals were obtained using either Dual Channel Non Linear Spectral Subtraction ‘DC-NLSS’ or Dual-Channel Multi-Band Spectral Subtraction ‘DC-MBSS’ algorithms. For performance evaluation, some objective speech assessment tests relying on Perceptual Evaluation of Speech Quality (PESQ) score and speech Itakura-Saito (IS) distortion measurement were performed to fix the optimal number of frequency band needed in DC-MBSS algorithm. In order to evaluate the speech intelligibility, subjective listening tests were assessed with 50 normal hearing listeners using a specific BCI simulator and with three deafened BCI patients. Experimental results, obtained using French Lafon database corrupted by an additive babble noise at different Signal-to-Noise Ratios (SNR), showed that DC-MBSS algorithm improves speech understanding better than DC-NLSS algorithm for single and multiple interfering noise sources. [Copyright &y& Elsevier]
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  Data: <i>Copyright of Applied Acoustics is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.apacoust.2011.06.010
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        Text: English
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      – SubjectFull: Spectral energy distribution
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      – SubjectFull: Signal processing
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              Text: Jan2012
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