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Adaptive Noise Canceller for Speech Enhancement Using 2-D Binary Mask
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 Title & Authors
Adaptive Noise Canceller for Speech Enhancement Using 2-D Binary Mask
Lee, Gihyoun; Lee, Jyung Hyun; Cho, Jin-Ho; Kim, Myoung Nam;
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Speech enhancement algorithm plays an important role in numerous speech signal processing applications. Over the last few decades, many algorithms have been studied for speech enhancement. The algorithms are based on spectral subtraction, Wiener filter, and subspace method etc. They have good performance of speech enhancement, but the performance can be deteriorated in specific noises or low SNR environment. In this paper, a new speech enhancement algorithms are proposed based on adaptive noise canceller. And the proposed algorithm improved performance of adaptive noise cancelling using 2-D binary mask. From objective experimental index, it is confirmed that the proposed algorithm is useful and has better performance than recently proposed speech enhancement algorithms.
Speech Enhancement;Adaptive Noise Canceller;2-D Binary Mask;
 Cited by
쉰목소리 완화를 위한 주파수 영역 음성 강조 필터 설계,김현태;이상협;

한국멀티미디어학회논문지, 2016. vol.19. 12, pp.1919-1926 crossref(new window)
Voice Boosting Filter Design in Frequency Domain for Relief of Husky Voice, Journal of Korea Multimedia Society, 2016, 19, 12, 1919  crossref(new windwow)
P.C. Loizou, Speech Enhancement: Theory and Practice, 2nd ed., CRC Press, Boca Raton, Florida, 2013.

M. Grimm and K. Kroschel, Robust Speech Recognition and Understanding, I-Tech Education and Publishing, Vienna Austria, 2007.

J. Proakis and D. Manolakis, Digital Signal Processing, 3rd ed., Prentice Hall, Upper Saddle Rive, NJ, 1996.

S.F. Boll, “Suppression of Acoustic Noise in Speech Using Spectral Subtraction,” IEEE Transaction on Acoustics Speech Signal Processing, Vol. 27, No. 2, pp. 113-120, 1979. crossref(new window)

N. Wiener, Extrapolation, Interpolation, and Smoothing of Stationary Time Series Vol. 2, MIT Press, Cambridge, 1949.

J. Beh and H. Ko. "A Novel Spectral Subtraction Scheme for Robust Speech Recognition: Spectral Subtraction Using Spectral Harmonics of Speech," Proceedings of International Conference on Acoustics, Speech, and Signal Processing, Vol. 1. pp. I-64, 2003.

Y. Hu and P. Loizou, “A Generalized Subspace Approach for Enhancing Speech Corrupted by Colored Noise,” IEEE Transaction on Speech and Audio Processing, Vol. 11, No. 4, pp. 334-341, 2003. crossref(new window)

J.F. Zhu and Y.D. Huang, “Improved Threshold Function of Wavelet Domain Signal DeNoising,” Proceeding of Internetional Conference on Wavelet Analysis and Pattern Recognition, pp. 14-17, 2013.

ITU, Perceptual Evaluation of Speech Quality (PESQ), and Objective Method for End-to-End Speech Quality Assessment of Narrow-band Telephone Networks and Speech Codecs, ITU-T Recommendation P.862, 2000.

Y. Hu and P. Loizou, “Speech Enhancement Based on Wavelet Thresholding the Multitaper Spectrum,” IEEE Transaction on Speech and Audio Processing, Vol. 12, No. 1, pp. 59-67, 2004. crossref(new window)

Y. Li and D. Wang, “On the Optimality of Ideal Binary Time–Frequency Masks,” Speech Communication, Vol. 51, No. 3, pp. 230-239, 2009. crossref(new window)

G.H. Lee, Y.J. Lee, J.H. Cho, M.N. Kim, “Voice Activity Detection Algorithm Using Fuzzy Membership Shifted C-means Clustering in Low SNR Environment,“ Journal of the Korea Multimedia Society, Vol. 17, No. 3, pp. 312-323, 2014. crossref(new window)

J.J. Godfrey, C.E. Holliman, and J. McDaniel, "SWITCHBOARD: Telephone Speech Corpus for Research and Development," Proceedings of International Conference on Acoustics, Speech, and Signal Processing, Vol. 1, pp. 517-520, 1992.

A. Varga and J.M.S. Herman, "Assessment for Automatic Speech Recognition: II. NOISEX-92: A Database and an Experiment to Study the Effect of Additive Noise on Speech Recognition Systems," Speech Communication, Vol. 12, No. 3, pp. 247-251, 1993. crossref(new window)