Adaptive Speech Spectrogram Approximation for Enhancement of Speech Signal

Abstract

The process of speech enhancement tends to decrease the noise with keeping undistorted speech signal amplitude. There are several benefits of speech processing systems which comes with the some challenges. In this paper, we proposed adaptive speech spectrogram approximation (ASSA) technique that used to tackle the de-noising and dereverberation in a single channel speech signal. The model is processed using sparse representation prototype in order to perform the de-noising process, where it remove the noise that present in speech signal more thoroughly.Where matrix factorization and SIFT is used to model the speech signal spectrogram, a time-varying filter is used to minimalize the noise more effectively.The noise adaptive model is implemented via iterative updating parameters in order to approximate the lower reverberant speech signal in a SIFT domain.Afterwards, the proposed ASSA technique compute the variation in estimated speech signal in order to decrease the noise components and to predict the final speech magnitude. In order to evaluate the performance of proposed system it is compared with state-of-art techniques using some performance metrics.