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Over 1.5 billion people worldwide live with hearing impairment. Despitevarious technologies that have been created for individuals with suchdisabilities, most of these technologies are either extremely expensive orinaccessible for everyday use in low-medium income countries. In order tocombat this issue, we have developed a new assistive device, EchoVest, forblind/deaf people to intuitively become more aware of their environment.EchoVest transmits vibrations to the user's body by utilizing transcutaneouselectric nerve stimulation (TENS) based on the source of the sounds. EchoVestalso provides various features, including sound localization, soundclassification, noise reduction, and depth perception. We aimed to outperformCNN-based machine-learning models, the most commonly used machine learningmodel for classification tasks, in accuracy and computational costs. To do so,we developed and employed a novel audio pipeline that adapts the AudioSpectrogram Transformer (AST) model, an attention-based model, for our soundclassification purposes, and Fast Fourier Transforms for noise reduction. Theapplication of Otsu's Method helped us find the optimal thresholds forbackground noise sound filtering and gave us much greater accuracy. In order tocalculate direction and depth accurately, we applied Complex Time Difference ofArrival algorithms and SOTA localization. Our last improvement was to use blindsource separation to make our algorithms applicable to multiple microphoneinputs. The final algorithm achieved state-of-the-art results on numerouscheckpoints, including a 95.7\% accuracy on the ESC-50 dataset forenvironmental sound classification.

EchoVest: Real-Time Sound Classification and Depth Perception Expressed through Transcutaneous Electrical Nerve Stimulation