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http://dx.doi.org/10.7776/ASK.2009.28.1.077

Noise-Biased Compensation of Minimum Statistics Method using a Nonlinear Function and A Priori Speech Absence Probability for Speech Enhancement  

Lee, Soo-Jeong (성균관대학교 정보통신공학부 BK21 사업단)
Lee, Gang-Seong (광운대학교 교양학부)
Kim, Sun-Hyob (광운대학교 컴퓨터공학과)
Abstract
This paper proposes a new noise-biased compensation of minimum statistics(MS) method using a nonlinear function and a priori speech absence probability(SAP) for speech enhancement in non-stationary noisy environments. The minimum statistics(MS) method is well known technique for noise power estimation in non-stationary noisy environments. It tends to bias the noise estimate below that of true noise level. The proposed method is combined with an adaptive parameter based on a sigmoid function and a priori speech absence probability (SAP) for biased compensation. Specifically. we apply the adaptive parameter according to the a posteriori SNR. In addition, when the a priori SAP equals unity, the adaptive biased compensation factor separately increases ${\delta}_{max}$ each frequency bin, and vice versa. We evaluate the estimation of noise power capability in highly non-stationary and various noise environments, the improvement in the segmental signal-to-noise ratio (SNR), and the Itakura-Saito Distortion Measure (ISDM) integrated into a spectral subtraction (SS). The results shows that our proposed method is superior to the conventional MS approach.
Keywords
Noise estimator; Speech enhancement; Adaptive threshold; Non-stationary noisy environment;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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