Browse > Article
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)
Publication Information
The Journal of the Acoustical Society of Korea / v.28, no.1, 2009 , pp. 77-83 More about this Journal
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)
연도 인용수 순위
1 I. Cohen, "Noise spectrum in adverse environments: imporved minima controlled recursive averaging," IEEE Trans. Speech and Audio Proc. 11(5), 466-475, 2003   DOI   ScienceOn
2 S. J. Lee and S. H. Kim, "Noise Suppression Using Normalized Time-Frequency Bin Average and Modified Gain Functionn for Speech Enhancement in Nonstationary Noisy Environment," J. Acoust. Soc. Kor. 27(1E), 1-10, 2008   ScienceOn
3 R. Matin, "Bias compensation methods for minimum statistics noise power spectral density estimation," SIGNAL PROCESSING 86, 1215-1229, 2006   DOI   ScienceOn
4 Y. Ephraim and D. Malah, "SpeechEnhancement using a minimum mean square error log-spectral amplitude estimator," IEEE Trans. Acoust. Speech Signal Process. 23(2), 443-445, 1985   DOI
5 C. L. PHILOPOS, SPEECH ENHANCEMENT (Theory and Practice, 1st edition, (CRC Press, Boca Raton, FL, 2007)
6 M. Berouti, M. Scrwartz, J. Makhoul, "Enhancement of speech corrupted by acoustic noise," Proc. IEEE Int. Conf. Acoust. Speech Signal Processing, 208-211, 1979
7 L. Lin, W.H. Holmes, and E. Ambikairajah, "Adaptive noise estimation algorithm for speech enhancement," Electronics Letters, 39(9), 754-755, 2003   DOI   ScienceOn
8 R. Sundarrajan, C.L. Philipos, "A noise-estimation algorithm for highly non-stationary environment," Speech Communication 48, 220-231, 2006   DOI   ScienceOn
9 A. Davis and S. Nordholm, "A low complexity statistical voice activity detector with performance comparisons to ITU-T/ ESTI voice activity detectors," Proc. IEEE Int. Conf. Information Communi. & Signal Process., 119-123, 2003   DOI
10 R. Martin, "Noise power spectral density estimation based on optimal smoothing and minimum statistics," IEEE Trans. Speech Audio Processing 9(5), 504-512, 2001   DOI   ScienceOn
11 I. Cohen, "Optimal Speech Enhancement Under Signal Presence Uncertainty Using Log-Spectral Amplitude Estimator," IEEE Signal Processing Letters 9(4), 113-116, 2002   DOI   ScienceOn
12 박윤식, 장준혁, "강인한 음성향상을 위한 Minimum Statistics와 Soft Decision의 확률적 결합의 새로운 잡음전력," 한국음향학회지 26(4), 153-158, 2007   과학기술학회마을   ScienceOn