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Optimal speech enhancement under signal presence uncertainty using Log Gabor Wavelet and Bayesian Joint Statistics

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Abstract

This paper investigates the problem of speech enhancement when only a single microphone is used and the statistics of the interfering noise and speech are not available a priori. Thus it seeks to address a pitfall of many current enhancement techniques and look towards a system which would have application in the real world. This paper focuses on Log Gabor Wavelet (LGW) based Long Term Squared Spectral Amplitude estimator using the Maximum a Posteriori (MAP) criterion. To begin with, long term cepstral mean subtraction technique with LGW is proposed to suppress telephone channel and handset effect from the speech signals. Then a novel speech enhancer by MAP based Bayesian Bivariate Model is developed to suppress the background noise. This work also introduces an inter-scale dependency between the coefficients and their parents by a Circularly Symmetric probability density function related to the family of Spherically Invariant Random Process (SIRPs). The corresponding joint estimator is derived by MAP estimation theory. The inter-scale noise variance of the coefficients is kept constant which gives closed form solution. Consideration of speech presence uncertainty (SPU) estimator is another contribution to the proposed estimator. Therefore, in this paper, the main contributions are; (i) combination of LGW, SIRPs and SPU for background noise reduction, (ii) LGW and Long Term Cepstral Mean Subtraction to reduce the effects of both telephone channel and handsets, (iii) circularly Symmetric probability density function to exploit the inter-scale dependency between the coefficients and their parents and corresponding joint estimators are derived by MAP estimation theory, (iv) the inter-scale noise variance of the coefficients is kept constant which gives closed form solution, (v) idea refines the estimate of the magnitudes by scaling them by the SPU probability. Extensive comparisons are done among the proposed and existing speech enhancement algorithms on NOIZEUS speech database which has different types of noise. We report the subjective and objective evaluations encompassing four classes of algorithms: spectral subtractive, subspace, statistical model based and Wiener type against the proposed methods. Experimental results show that the proposed estimator yields a higher improvement in Segmental SNR (SSNR), lower Log Area Ratio (LAR), Weighted Spectral Slope (WSS) distortion, higher Perceptual Evaluation of Speech Quality (PESQ) and Mean Opinion Score (MOS) compared to the existing speech enhancement algorithms. For SSNR measure, the proposed methods show 2 dB of improvement than existing methods for almost every Noise sources. For MOS measure, the proposed methods show improvement than existing methods for almost every Noise sources. Therefore the proposed methods are aiming to enhance the speech quality as well as intelligibility at a time.

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  1. Indian Institute of Technology, Kharagpur, West Bengal, India

    Suman Senapati

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  1. Suman Senapati
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Senapati, S. Optimal speech enhancement under signal presence uncertainty using Log Gabor Wavelet and Bayesian Joint Statistics. Int J Speech Technol 16, 439–459 (2013). https://doi.org/10.1007/s10772-013-9195-3

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