Noise Estimation and Suppression Using Nonlinear Function with A Priori Speech Absence Probability in Speech Enhancement
This paper proposes a noise-biased compensation of minimum statistics (MS) method using a nonlinear function and a priori speech absence probability (SAP) for speech enhancement in highly nonstationary noisy environments. The MS method is a well-known technique for noise power estimation in nonstati...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2016-01-01
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| Series: | Journal of Sensors |
| Online Access: | http://dx.doi.org/10.1155/2016/5352437 |
| Summary: | This paper proposes a noise-biased compensation of minimum statistics (MS) method using a nonlinear function and a priori speech absence probability (SAP) for speech enhancement in highly nonstationary noisy environments. The MS method is a well-known technique for noise power estimation in nonstationary noisy environments; however, it tends to bias noise estimation below that of the true noise level. The proposed method is combined with an adaptive parameter based on a sigmoid function and a priori SAP for residual noise reduction. Additionally, our method uses an autoparameter to control the trade-off between speech distortion and residual noise. We evaluate the estimation of noise power in highly nonstationary and varying noise environments. The improvement can be confirmed in terms of signal-to-noise ratio (SNR) and the Itakura-Saito Distortion Measure (ISDM). |
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| ISSN: | 1687-725X 1687-7268 |