| Autor: |
Qiquan Zhang, Mingjiang Wang, Yun Lu, Muhammad Idrees, Lu Zhang |
| Jazyk: |
angličtina |
| Rok vydání: |
2019 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 7, Pp 80985-80999 (2019) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2019.2923680 |
| Popis: |
Estimation of the noise power spectral density (PSD) plays a critical role in most existing single-channel speech enhancement algorithms. In this paper, we present a novel noise PSD tracking algorithm, which employs a log-spectral power minimum mean square error (MMSE) estimator. This method updates the noise PSD estimate by performing a temporal recursive averaging of log-spectral MMSE estimate of the current noise power to reduce the risk of speech leakage into noise estimate. A smoothing parameter used in the recursive operation is adjusted by speech presence probability (SPP). In this method, a spectral nonlinear weighting function is derived to estimate the noise spectral power which depends on the a priori and the a posteriori signal-to-noise ratio (SNR). An extensive performance comparison has been carried out with several state-of-the-art noise tracking algorithms, i.e., Minimum Statistics (MS), modified minima controlled recursive averaging algorithm (MCRA-2), MMSE-based method, and SPP-based method. It is clear from experimental results that the proposed algorithm exhibits more excellent noise tracking capability under various nonstationary noise environments and SNR levels. When employed in a speech enhancement framework, improved speech enhancement performance in terms of the segmental SNR (segSNR) improvements and three objective composite metrics is observed. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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