Enhancing Speech Quality Using Artificial Bandwidth Expansion with Deep Shallow Convolution Neural Network Framework
Abstract
Speech processing is an important application area of digital signal processing that helps examine and analyze the speech signal. In this processing, speech enhancement is an essential factor because it improves the quality of the signal that helps resolve the communication challenges. Different speech enhancement algorithms are utilized in the research field, but limited processing capabilities, maximum microphone distance, and voice-first I.O. interfaces create the computation complexity. In this paper, speech enhancement is done in two steps. In an initial step, spectral subtraction method is applied to LJ Speech dataset. In the first stage, noise spectrum is estimated during pauses and it is subtracted from the noisy speech signal to obtain the clean speech signal. However, spectral subtraction method still introduces artificial noise and narrow-band noise in the spectrum. Hence, artificial bandwidth expansion with a deep shallow convolution neural network (ABE-DSCNN) is implemented as a second stage in the paper. Further, developed system is compared with conventional enhancement approaches such as deep learning network (DNN), neural beam forming (NB) and generative adversarial network (GAN). The experimental results show that an ABS-DSCNN provides 4% increase of PSEQ and error rate improved by 40% to 56% with respect to the other existing algorithms for 1000 speech samples. Hence, the paper concludes that ABE-DSCNN approach effectively improves the speech quality.
Communicated by Hongjing Liang
