Rate-constrained source separation for speech enhancement in wireless-communicated binaural hearing aids

A recent trend in hearing aids is the connection of the left and right devices to collaborate between them. Binaural systems can provide natural binaural hearing and support the improvement of speech intelligibility in noise, but they require data transmission between both devices, which increases the power consumption. This paper presents a novel sound source separation algorithm for binaural speech enhancement based on supervised machine learning and time-frequency masking. The system is designed considering the power restrictions in hearing aids, constraining both the computational cost of the algorithm and the transmission bit rate. The transmission schema is optimized using a tailored evolutionary algorithm that assigns a different number of bits to each frequency band. The proposed algorithm requires less than 10% of the available computational resources for signal processing and obtains good separation performance using bit rates lower than 64 kbps.