A Novel RLS Based Adaptive Filtering Method for Speech Enhancement

Speech enhancement is a long standing problem with numerous applications like teleconferencing, VoIP, hearing aids and speech recognition. The motivation behind this research work is to obtain a clean speech signal of higher quality by applying the optimal noise cancellation technique. Real-time adaptive filtering algorithms seem to be the best candidate among all categories of the speech enhancement methods. In this paper, we propose a speech enhancement method based on Recursive Least Squares (RLS) adaptive filter of speech signals. Experiments were performed on noisy data which was prepared by adding AWGN, Babble and Pink noise to clean speech samples at -5dB, 0dB, 5dB and 10dB SNR levels. We then compare the noise cancellation performance of proposed RLS algorithm with existing NLMS algorithm in terms of Mean Squared Error (MSE), Signal to Noise ratio (SNR) and SNR Loss. Based on the performance evaluation, the proposed RLS algorithm was found to be a better optimal noise cancellation technique for speech signals.

Optimum Cascaded Design for Speech Enhancement Using Kalman Filter

Speech enhancement is the process of eliminating noise and increasing the quality of a speech signal, which is contaminated with other kinds of distortions. This paper is on developing an optimum cascaded system for speech enhancement. This aim is attained without diminishing any relevant speech information and without much computational and time complexity. LMS algorithm, Spectral Subtraction and Kalman filter have been deployed as the main de-noising algorithms in this work. Since these algorithms suffer from respective shortcomings, this work has been undertaken to design cascaded systems in different combinations and the evaluation of such cascades by qualitative (listening) and quantitative (SNR) tests.