Single Channel Speech Enhancement Using Spectral Subtraction Based on Minimum Statistics

Sammanfattning: Speech is an elementary source of human interaction. The quality and intelligibility of speech signals during communication are generally degraded by the surrounding noise. Corrupted speech signals need therefore to be enhanced to improve quality and intelligibility. In the field of speech processing, much effort has been devoted to develop speech enhancement techniques in order to restore the speech signal by reducing the amount of disturbing noise. This thesis focuses on a single channel speech enhancement technique that performs noise reduction by spectral subtraction based on minimum statistics. Minimum statistics means that the power spectrum of the non-stationary noise signal is estimated by finding the minimum values of a smoothed power spectrum of the noisy speech signal and, thus, circumvents the speech activity detection problem. The performance of the spectral subtraction method is evaluated using single channel speech data and for a wide range of noise types with various noise levels. This evaluation is used in order to find optimum method parameter values, thereby improving this algorithm to make it more appropriate for speech communication purposes. The system is implemented in MATLAB and validated by considering different performance measure and for different Signal to Noise Ratio Improvement (SNRI) and Spectral Distortion (SD). The SNRI and SD were calculated for different filter bank settings such as different number of subbands and for different decimation and interpolation ratios. The method provides efficient speech enhancement in terms of SNRI and SD performance measures.