Vector-Quantized Zero-Delay Deep Autoencoders for the Compression of Electrical Stimulation Patterns of Cochlear Implants using STOI

Abstract

Cochlear implants (CIs) are battery-powered, surgically implanted hearing-aids capable of restoring a sense of hearing in people suffering from moderate to profound hearing loss. Wireless transmission of audio from or to signal processors of cochlear implants can be used to improve speech understanding and localization of CI users. Data compression algorithms can be used to conserve battery power in this wireless transmission. However, very low latency is a strict requirement, limiting severly the available source coding algorithms. Previously, instead of coding the audio, coding of the electrical stimulation patterns of CIs was proposed to optimize the trade-off between bit-rate, latency and quality. In this work, a zero-delay deep autoencoder (DAE) for the coding of the electrical stimulation patters of CIs is proposed. Combining for the first time bayesian optimization with numerical approximated gradients of a nondifferential speech intelligibility measure for CIs, the short-time intelligibility measure (STOI), an optimized DAE architecture was found and trained that achieved equal or superior speech understanding at zero delay, outperforming well-known audio codecs. The DAE achieved reference vocoder STOI scores at 13.5 kbit/s compared to 33.6 kbit/s for Opus and 24.5 kbit/s for AMR-WB.