Use of radial basis function network with discrete wavelet transform for speech enhancement
Rashmirekha Ram, Mihir Narayan Mohanty · International Journal of Computational Vision and Robotics · 2019
Neural network has occupied a very good position in the field of detection, recognition and classification. However the use of these models for signal enhancement is a new direction of research. In this paper, neural network is used to enhance the quality of the speech. The efficient model radial basis function network (RBFN) is chosen for enhancement of the noisy signals. Wavelet Transform is used for decomposition of signal. It works in both the ways. In first stage, the noise from the input signal is reduced. Next to it, these coefficients are used as weights of the RBFN model that makes faster processing as compared to use of random weights. The output of the proposed model is measured in terms of signal to noise ratio (SNR), segmental signal to noise ratio (SegSNR) and perceptual evaluation of speech quality (PESQ). The performance of the proposed method found excellent and is exhibited in the result section.