Efficient two-microphone speech enhancement using basic recurrent neural network cell for hearing and hearing aids.

This work presents a two-microphone speech enhancement (SE) framework based on basic recurrent neural network (RNN) cell. The proposed method operates in real-time, improving the speech quality and intelligibility in noisy environments. The RNN model trained using a simple feature set-real and imaginary parts of the short-time Fourier transform (STFT) are computationally efficient with a minimal input-output processing delay. The proposed algorithm can be used in any stand-alone platform such as a smartphone using its two inbuilt microphones. The detailed operation of the real-time implementation on the smartphone is presented. The developed application works as an assistive tool for hearing aid devices (HADs). Speech quality and intelligibility test results are used to compare the proposed algorithm to existing conventional and neural network-based SE methods. Subjective and objective scores show the superior performance of the developed method over several conventional methods in different noise conditions and low signal to noise ratios (SNRs).