Abstract
In Bayesian approaches for speech enhancement, the enhanced speech is estimated by minimizing the Bayes risk. In detail, an estimate of the clean speech is derived by minimizing the expectation of a cost function. Various estimators have been derived by the classic cost function, squared-error cost function, and “hit-or-miss” function. However, absolute error function was paid less attention. In this paper, we consider a magnitude-squared spectrum (MSS) motivated estimator for speech enhancement based on statistics and Bayesian cost function in the frequency domain. Specifically, we derive a novel estimator of which the cost function is the absolute error distortion measure of the MSS. By studying experimental results with NOIZEUS database, we find that the performance of the proposed scheme can achieve a significant noise reduction and a better speech quality as compared to minimum mean-squared error (MMSE) estimator of the MSS. Ill. 1, bibl. 14, tabl. 2 (in English; abstracts in English and Lithuanian) DOI: http://dx.doi.org/10.5755/j01.eee.122.6.1812
Highlights
The problem of improving the quality and intelligibility of speech in noisy environments has attracted a great deal of interest in a long time
The maximum a posteriori (MAP) [6] estimator, minimum mean square error (MMSE) and maximum likelihood (ML) [7] estimators can be derived from the different Bayes risk cost functions
It is not difficult to notice that the Bayesian estimators based on perceptually motivated cost functions in place of traditional cost function are tightly related to the Byes risk [8,9,10]
Summary
School of Information Science and Engineering, Hunan University, Lushan South Rood, Changsha 410082, P. R. China, Jiangsu Provincial Key Lab of Image Processing & Image Communications Nanjing University of Posts and Telecommunicationsy, Nanjing 310002, P. R. China http://dx.doi.org/10.5755/j01.eee.122.6.1812
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