• The Journal of the Acoustical Society of Korea
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• v.27 no.5
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• pp.256-261
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• 2008

In this paper, we propose a novel approach to improve the performance of minima controlled recursive averaging (MCRA) which is based on the conditional maximum a posteriori criterion. A crucial component of a practical speech enhancement system is the estimation of the noise power spectrum. One state-of-the-art approach is the minima controlled recursive averaging (MCRA) technique. The noise estimate in the MCRA technique is obtained by averaging past spectral power values based on a smoothing parameter that is adjusted by the signal presence probability in frequency subbands. We improve the MCRA using the speech presence probability which is the a posteriori probability conditioned on both the current observation the speech presence or absence of the previous frame. With the performance criteria of the ITU-T P.862 perceptual evaluation of speech quality (PESQ) and subjective evaluation of speech quality, we show that the proposed algorithm yields better results compared to the conventional MCRA-based scheme.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.4
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• pp.132-138
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• 2009

In this paper, we propose a novel approach to improve the performance of minima controlled recursive averaging (MCRA) which is based on the second-order conditional maximum a posteriori (CMAP). From an investigation of the MCRA scheme, it is discovered that the MCRA method cannot take full consideration of the inter-frame correlation of voice activity since the noise power estimate is adjusted by the speech presence probability depending on an observation of the current frame. To avoid this phenomenon, the proposed MCRA approach incorporates the second-order CMAP criterion in which the noise power estimate is obtained using the speech presence probability conditioned on both the current observation and the speech activity decisions in the previous two frames. Experimental results show that the proposed MCRA technique based on second-order conditional MAP yields better results compared to the conventional MCRA method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.6
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• pp.1120-1125
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• 2010

In the speech system, for example hearing aid as well as speech communication, speech quality is degraded by environmental noise. In this study, to enhance the speech quality which is degraded by environmental speech, we proposed an algorithm to reduce the noise and reinforce the speech. The minima controlled recursive averaging (MCRA) algorithm is used to estimate the noise spectrum and spectral weighting factor is used to reduce the noise. And partial masking effect which is one of the human hearing properties is introduced to reinforce the speech. Then we compared the waveform, spectrogram, Perceptual Evaluation of Speech Quality (PESQ) and segmental Signal to Noise Ratio (segSNR) between original speech, noisy speech, noise reduced speech and enhanced speech by proposed method. As a result, enhanced speech by proposed method is reinforced in high frequency which is degraded by noise, and PESQ, segSNR is enhanced. It means that the speech quality is enhanced.