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http://dx.doi.org/10.7776/ASK.2008.27.7.386

Adaptive Threshold for Speech Enhancement in Nonstationary Noisy Environments  

Lee, Soo-Jeong (광운대학교 음성신호처리연구실)
Kim, Sun-Hyob (광운대학교 컴퓨터공학과)
Publication Information
The Journal of the Acoustical Society of Korea / v.27, no.7, 2008 , pp. 386-393 More about this Journal
Abstract
This paper proposes a new approach for speech enhancement in highly nonstationary noisy environments. The spectral subtraction (SS) is a well known technique for speech enhancement in stationary noisy environments. However, in real world, noise is mostly nonstationary. The proposed method uses an auto control parameter for an adaptive threshold to work well in highly nonstationary noisy environments. Especially, the auto control parameter is affected by a linear function associated with an a posteriori signal to noise ratio (SNR) according to the increase or the decrease of the noise level. The proposed algorithm is combined with spectral subtraction (SS) using a hangover scheme (HO) for speech enhancement. The performances of the proposed method are evaluated ITU-T P.835 signal distortion (SIG) and the segment signal to-noise ratio (SNR) in various and highly nonstationary noisy environments and is superior to that of conventional spectral subtraction (SS) using a hangover (HO) and SS using a minimum statistics (MS) methods.
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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1 A. Davis and S. Nordholm, "A Low Complexity Statistical Voice Activity Detector with Performance Comparisons to ITU-T/ ETSI Voice Activity Detectors, Proc. IEEE Int. Conf. Information Communi. & Signal Process., 119-123. 2003
2 C. L. PHILOPS, SPEECH ENHANCEMENT (Theory and Practice), 1st edition. (CRC Press, Boca Raton, FL, 2007), pp.495-498
3 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
4 L. LIN, W.H. HOLMES, and E. AMBIKAIRAJAH, "Adaptive noiseestimation algorithm for speech enhancement", ELECTRONICS LETTERS, 39(9), 754-755, 2003   DOI   ScienceOn
5 M. Berouti, M, Scrwartz, J. Makhoul, "Enhancement of speech corrupted by acoustic noise", Proc. IEEE int. Conf.Acoust. Speech Signal Processing, 208-211, 1979
6 S. J. Lee and S. H. Kim, "Noise Suppression Using Normalized Time-Frequency Bin Average and Modified Gain Function for Speech Enhancement in Nonstationary Noisy Environments", THE JOURNAL OF THE ACOUSTICAL SOCIETY OF KOREA, 27(1E), 1-10, 2008   과학기술학회마을
7 박윤식, 장준혁, "강인한 음성향상을 위한 Minimum Statistics 와 Soft Decision의 확률적 결합의 새로운 잡음전력", 한국음향 학회지, 26(4), 153-158, 2007   과학기술학회마을
8 I. Cohen, "Noise Spectrum in Adverse Enviroments: Improved Minima Controlled Recursive Averaging", IEEE Trans. Speech and Audio Proc., 11(5), 466-475, 2003
9 ITU-T, "Subjective test methodology for evaluating speech communication systems that include noise suppression algorithm", ITU-T Recommendation P.835, 2003
10 R. SUNDARRAJAN, C.L. PHILIPOS, "A noise-estimation algorithm for highly non-stationary environment", SPEECH COMMUNICATION., 48, 220-231, 2006   DOI   ScienceOn