Single channel speech enhancement using Deep Neural Networks

Speech enhancement is an important first step in many applications like mobile communication, Speech recognition, hearing aids etc. Traditionally, speech enhancement was viewed as a pure signal processing problem, and several methods have been proposed to design linear filters to suppress the noise. However, with the advent of deep neural networks, speech enhancement has been viewed as a machine learning problem, which aims at learning a nonlinear model that maps the noisy speech signal to the clean speech signal. In this thesis, we provide an overview of existing signal processing approaches for single channel speech enhancement and compare their performance with the DNN counterparts. Even though the DNN based approaches provide significant performance improvements, they do not use phase information in the speech signal. In this work, We propose a speech enhancement framework using DNNs, which incorporates both magnitude (Cochleagram) and phase (Instantaneous frequency) information. Experimental results demonstrate that proposed framework achieves improved performance, especially at lower SNRs, over the conventional systems.