Adaptive Weiner filtering with AR-GWO based optimized fuzzy wavelet neural network for enhanced speech enhancement

Speech signal enhancement is a subject of study in which a large number of researchers are working to improve the quality and perceptibility of speech signals. In the existing Kalman Filter method, the short-time magnitude or power spectrum due to random variations of noise was a serious problem and the signal-to-noise ratio was very low. This issue severely reduced the perceived qualityand intelligibility of enhanced speech. Thus, this paper intent to develop an improved speech enhancement model and it includes"training phase and testing phase". In the training phase, the input noise corrupted signal is initially fed as input to both STFT-based noise estimation and NMF-based spectrum estimation forestimating the noise spectrum and signal spectrum, respectively. The obtained noise spectrum and the signal spectrum are fed as input to the Wiener filter and these filtered signals are subjected to Empirical Mean Decomposition (EMD).Since, tuning factor plays a key role in Wiener filter, it has to be determined for each signal and from the denoised signal the bark frequency is evaluated. The computed bark frequency is fed as input to the learning algorithm referred as Fuzzy Wavelet Neural Network (FW-NN)for detecting the suited tuning factor η for the entire input signal in Wiener filter.An Adaptive Randomized Grey Wolf Optimization (AR-GWO) is proposed for proper tuning of the tuning factor η referred as tuned tuning factor (η ). The proposed AR-GWO is the improved version of standard Grey wolf optimization (GWO). In the testing phase, the training is accomplished initially and from which the tuning factor is gathered for each of the relevant input signal. Then, the properly tuned tuning factor (η ) from FW-NN is fed as input to EMD via adaptive wiener filter for decomposing the spectral signal and the output of EMD is denoised enhanced speech signal. At last, the performance of the adopted approach is evaluated to the existing approaches in terms of various metrics. In particular, the computation time of the adopted AR-GWO model is 34.07%, 43.57%, 28.86%, 38.88%, and 16.03% better than the existing GA, ABC, PSO, FF, and GWO approaches respectively.