A Novel Magnitude-Squared Spectrum Cost Function for Speech Enhancement

摘要

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 existence of noise is inevitable in real-world application of speech processing. In particular, speech coders and speech recognition systems might be rendered useless in the presence of background noise. Numerous techniques have been developed, and conventional speech enhancement algorithms basically consist of four classes of algorithms, including spectral subtraction [1], subspace [2], statistical model based [3] and Wiener filter based algorithms [4]. The well-known Ephraim-Malah algorithm which base on statistical model is an MMSE [3] estimator for the speech DFT amplitude. In this study, we also choose the Byes risk as the basis since it is the most fundamental statistical model approach, and many algorithms are closely connected to this technique [5]. Minimizing the Byes risk for a given cost function results in a variety of estimators. In fact, 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. Also 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 [8-10]. In summary, different Bayesian estimators can be derived depending on the choice of the cost function. In recent years, Yang Lu and Loizou et al [11] propose a new speech enhancement algorithm which assumes that the magnitude-square spectrum of the noisy speech signal can be computed as the sum of the (clean) signal and noise magnitude-squared spectra, and finally, they derive a MMSE-MSS estimator which uses the squared-error cost function. Motivated by the previously mentioned assumption, we derive a novel speech enhancement by using other distortion measure in this paper. Results show that the proposed estimator yielded lower residual noise and lower speech distortion than the conventional MMSE-MSS estimator, in terms of yielding better speech quality. This paper is organized as follows. In section 2, the background information of Bayes risk is given. In section 3, the proposed algorithm is presented. The experimental results of comparing the algorithm proposed in this paper with other algorithms are also presented in section 4. Finally, our work of this paper is summarized in the last section.