An Improved Noise Spectral Estimation Algorithm Based on the Weighted Minimum Statistics

Niu Tong; Zhang Lian-hai; Qu Dan

doi:10.3724/SP.J.1146.2008.00467

Volume 31 Issue 5

Dec. 2010

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Niu Tong, Zhang Lian-hai, Qu Dan. An Improved Noise Spectral Estimation Algorithm Based on the Weighted Minimum Statistics[J]. Journal of Electronics & Information Technology, 2009, 31(5): 1166-1169. doi: 10.3724/SP.J.1146.2008.00467

Citation:

Niu Tong, Zhang Lian-hai, Qu Dan. An Improved Noise Spectral Estimation Algorithm Based on the Weighted Minimum Statistics[J]. Journal of Electronics & Information Technology, 2009, 31(5): 1166-1169. doi: 10.3724/SP.J.1146.2008.00467

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An Improved Noise Spectral Estimation Algorithm Based on the Weighted Minimum Statistics

doi: 10.3724/SP.J.1146.2008.00467

Received Date: 2008-04-22
Rev Recd Date: 2008-09-19
Publish Date: 2009-05-19

Abstract

Abstract

As the noise spectral estimation based on the minimum statistics introduces significant tracking latency when the noise spectral rises, an improved algorithm based on the weight minimum statistics is presented. Analyzing the influence of weight on the noise spectral estimation based on the minimum statistics, three kinds of typical simple curves are used to compute the weight, and the experiment shows that the weight computed by the cosine curve is the best. The simulation results show that the improved algorithm traces the change of noise spectral quickly in most cases, improves the accuracy of the noise spectral estimation and the quality of speech in the non-stationary noise environment.

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References

Martin R, Malah D, and Richard V, et al.. A noise reductionpreprocessor for mobile voice communication [J].EURASIPJournal on Applied Signal Processing.2004, 2004(1):1046-1058[2]Loizou P C. Speech Enhancement: Theory and Practice [M].Boca Raton, FL: CRC Press, 2007, Chapter 9.[3]Martin R. Noise power spectral density estimation based onoptimal smoothing and minimum statistics [J].IEEE. Trans.on Speech, and Audio Processing.2001, 9(5):504-512[4]Hu Y and Loizou P C. Subjective comparison of speechenhancement algorithms [C]. Proceedings of ICASSP-2006,Toulouse, France, May 2006, vol.I: 153-156.[5]Hendriks R C, Jensen J, and Heusdens R. DFT Domainsubspace based noise tracking for speech enhancement [C].INTERSPEECH 2007, Antwerp, Belgium, August 2007:830-834.[6][6] ITU. Perceptual evaluation of speech quality (PESQ), andobjective method for end-to-end speech quality assessment ofnarrowband telephone networks and speech codes [S]. ITU-TRecommendation P. 862, 2000.[7]Hu Y and Loizou P C. Evaluation of objective qualitymeasures for speech enhancement [J].IEEE Trans. on Audio,Speech, Language Process.2008, 16(1):229-238

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