Speech Enhancement Using Recursive Least Square Based on Real-time adaptive filtering algorithm

摘要

In real time environment a speech signal is often corrupted and losses its characteristics either by natural disturbances or anything. The key aim of our planned algorithm is toward increase the speech intelligibility and quality. In order to do that a filter has been developed using Recursive Least Squares (RLS) algorithms and Least Mean Square (LMS). Real-time adaptive filtering algorithms are one of the best methods used for the speech enhancement methods. In this research work we have proposed the recursive least square which is under adaptive filtering method for the enhancement of the speech signal. Initially we compare the performance of noise cancellation of the proposed Recursive least square which uses objective evaluations that is based on wavelet based speech enhancement like Signal to noise ratio Loss, Signal to Noise ratio and Mean Squared Error. Based on the Objective and Subjective evaluation, it was found that this algorithm clearly in increases the intelligibility and removes the corrupted noise in the waveforms. There are different types of filters like Kalman filter, Wiener filter, Spectral subtraction, and notch filter and wavelet methods. The performance of every filter depends upon the intelligibility also excellence of the speech signal. The reduction or augmentation in the SNR ratio is the main aim of the most methods. Adaptive filtering is a technique which uses certain predefined criterion like the estimated mean squared error or the correlation has to be considered for the analyses of the waveform. In this adaptive filter, we use coefficients with weights and an adaptive algorithm updates are made available.