Dual Microphone Speech Enhancement Algorithms on Android-Based Devices for Hearing Study

Speech Enhancement (SE) is a key module in the Hearing Aid (HA) signal processing pipeline and improves the listening comfort. Over the last few decades, researchers have developed many single and dual-microphone SE techniques. In this thesis, two novel dual-channel SE techniques have been proposed and are implemented on Android-based smartphones as an assistive device for HA. In the first algorithm, the coherence between speech and noise signals is used to obtain an SE gain function, in combination with a Super-Gaussian Joint Maximum a Posteriori (SGJMAP) single microphone SE gain function. The second technique uses the Minimum Variance Distortionless Response (MVDR) as a Signal to Noise Ratio (SNR) booster for the SE method. The considered SE gain is based on the Log Spectral Minimum Mean Square Error Amplitude Estimator (Log-MMSE) to improve the speech quality in the presence of different background noise. Objective evaluation and subjective results of the developed methods show significant improvements in speech quality and intelligibility in comparison with existing SE methods.