Development of CNN-Based Cochlear Implant and Normal Hearing Sound Recognition Models Using Natural and Auralized Environmental Audio

Abstract

Restoration of auditory function among hearing impaired individuals using Cochlear Implant (CI) technology has contributed significantly towards an improved quality of life. CI users experience greater challenges in recognizing speech effectively in noisy, reverberant, or time-varying diverse environments. Most CI research efforts focus on enhancing speech perception and environmental sound awareness has received little or no attention. 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. Natural and auralized sound representations are then applied in order to develop NH and CI sound recognition models. Comparative assessment of environmental sound recognition is carried out by analyzing f1-scores and other performance characteristics. Benefits stemming from this research can help CI researchers improve sound recognition performance, develop novel sound processing algorithms, exclusively for environmental sounds, and identify optimal CI electrical stimulation characteristics to enhance sound perception. Among CI users, improvement in environmental sound awareness contributes to improved quality of life.