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Speech Enhancement Using Modified Modulation Magnitude Estimation-Based Spectral Subtraction Algorithm

  • Research Article - Electrical Engineering
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Abstract

In this paper, noise variance is estimated from the noisy spectrum using the proposed noise estimation algorithm. In addition, the speech signal is enhanced by the modified modulation magnitude estimation (MMME)-based spectral subtraction algorithm. Chi-square distribution-based noise estimation algorithm is proposed in this work. During the speech presence period, the noise variance is estimated by recursively smoothing and averaging the noisy spectrum. The speech signal is then enhanced by estimating the modulation magnitude spectrum, which depends on a posterior SNR instead of a priori SNR. The optimal speech presence probability q m is found as 0.9, and simulations are carried out for this optimal value. Objective and subjective measures for the various noises are made and compared between the existing and proposed algorithms. From the experimental results, it is observed that the proposed noise estimation method comparably reduces mean square error (MSE) and LogErr to the earlier methods under various noise conditions with different input SNR levels. In addition, the proposed spectral subtraction method noticeably increases the dB value of peak signal to noise ratio, segmental SNR and segmental SNR improvement (ΔSNRseg) contrasting various existing spectral subtraction algorithms with the optimal q m value. Also, it improves the mean opinion score value and reduces the log spectral distance and MSE when compared to that of various existing ones. Hence, it reveals that the MMME-based spectral subtraction with proposed noise estimation algorithm reduces the speech distortion and residual noise as compared to existing methods.

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Kalamani, M., Valarmathy, S. & Krishnamoorthi, M. Speech Enhancement Using Modified Modulation Magnitude Estimation-Based Spectral Subtraction Algorithm. Arab J Sci Eng 39, 8965–8978 (2014). https://doi.org/10.1007/s13369-014-1446-3

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  • DOI: https://doi.org/10.1007/s13369-014-1446-3

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