Enhancing Cochlear Implant Signal Coding with Scaled Dot-Product Attention
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Abstract
Cochlear implants (CIs) play a vital role in restoring hearing for individuals with severe to profound sensorineural hearing loss by directly stimulating the auditory nerve with electrical signals. While traditional coding strategies, such as the advanced combination encoder (ACE), have proven effective, they are constrained by their adaptability and precision. This paper investigates the use of deep learning (DL) techniques to generate electrodograms for CIs, presenting our model as an advanced alternative. We compared the performance of our model with the ACE strategy by evaluating the intelligibility of reconstructed audio signals using the short-time objective intelligibility (STOI) metric. The results indicate that our model achieves a STOI score of 0.6031, closely approximating the 0.6126 score of the ACE strategy, and offers potential advantages in flexibility and adaptability. This study underscores the benefits of incorporating artificial intelligent (AI) into CI technology, such as enhanced personalization and efficiency.
