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Evaluating Performance of Speech Enhancement Algorithms
| Content Provider | Scilit |
|---|---|
| Author | Loizou, Philipos C. |
| Copyright Year | 2013 |
| Description | In the previous chapter, we described the Wiener lter approach to speech enhancement. This approach derives in the mean-square sense the optimal complex discrete Fourier transform (DFT) coefcients of the clean signal. The Wiener lter approach yields a linear estimator of the complex spectrum of the signal and is optimal in the minimum mean-square-error (MMSE) sense when both the (complex) noise and speech DFT coefcients are assumed to be independent Gaussian random variables. In this chapter, we focus on nonlinear estimators of the magnitude (i.e., the modulus of the DFT coefcients) rather than the complex spectrum of the signal (as done by the Wiener lter), using various statistical models and optimization criteria. These nonlinear estimators take the probability density function (pdf) of the noise and the speech DFT coefcients explicitly into account and use, in some cases, non-Gaussian prior distributions. These estimators are often combined with soft-decision gain modications that take the probability of speech presence into account. Book Name: Speech Enhancement |
| Related Links | https://content.taylorfrancis.com/books/download?dac=C2011-0-16639-5&isbn=9780429096181&doi=10.1201/b14529-13&format=pdf |
| Ending Page | 439 |
| Page Count | 1 |
| Starting Page | 439 |
| DOI | 10.1201/b14529-13 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2013-02-25 |
| Access Restriction | Open |
| Subject Keyword | Book Name: Speech Enhancement Audiology and Language Function Models Speech Optimal Account Dft Coefcients Wiener Lter |
| Content Type | Text |
| Resource Type | Chapter |
