Development Of Cnn-Based Cochlear Implant And Normal Hearing Sound Recognition Models Using Natural And Auralized Environmental Audio

Restoration of auditory function among hearing impaired individuals using Cochlear Implant (CI) technology has contributed significantly towards an improved quality of life. Most clinical studies and research efforts in CI, including Machine Learning (ML) techniques, are focused on enhancing speech perception while limited research efforts have considered environmental sound awareness. It is also well known that CI users experience greater challenges in effective speech recognition in noisy, reverberant, or time-varying diverse environments. This study focuses on a comparative analysis of normal hearing (NH) vs. CI environmental sound recognition using classifiers trained on learned sound representations using a CNN-based sound event model. Sounds experienced by CI listeners are recreated by auralizing electrical stimuli. CCi-MOBILE is used to generate electrical stimuli and Braecker Vocoder is used for auralization. Representations of natural and auralized sounds are used to model NH and CI sound recognition systems respectively. Information related to environmental sound is extracted by analyzing f1-scores and other performance characteristics. Benefits stemming from this research can help CI researchers advance sound recognition performance, develop novel sound processing algorithms and identify optimal CI electrical stimulation characteristics. Among CI users, improvement in environmental sound awareness contributes to improved quality of life.