Analysis of Optimized Spectral Subtraction Method for Single Channel Speech Enhancement

Speech is the primary entity for personal communication however ambient quality generally impairs speech signal quality and understanding of communication. Therefore, it is required that the distorted speech signal be improved in its quality and comprehension. In the field of speech processing, great efforts have been made to develop speech enhancement techniques that restore speech signals by reducing the amount of interfering noise. This work focuses on a critical analysis of single channel speech enhancement technique that performs noise reduction through spectral subtraction based on minimal statistics. Minimal statistics implies estimating the power spectrum of a non-standard noise signal by avoiding the problem of detecting speech activity by finding the smallest value for a smooth power spectrum of a noisy speech signal. The performance of the spectral subtraction method is evaluated over a wide range of noise types with varying sound levels using single channel speech data. This estimator is used to find the optimal value for the method parameter and improve this algorithm to make it more suitable for voice communication purposes. The system can be implemented in MATLAB and also validated against a variety of performance measures and various improvements in signal-to-noise ratio (SNRI) and spectral distortion (SD). This approach provides effective speech enhancement in SNRI and SD performance metrics. A comparatively new method has been proposed in this paper named Spectral Statistics Based on Minimum Statistics (SSBMS) which customarily follows the transient noise and provides a better response in the process of speech enhancement.