Enhancing Environmental Awareness of Deaf Individuals Using Deep Learning-Based Audio Classification

Abstract - Environmental sounds play a crucial role in human perception and situational awareness. However, individuals who are deaf or hard of hearing face significant challenges in detecting and interpreting such auditory cues, potentially compromising their safety and independence in everyday environments. This research presents a deep learning-based approach to real-time audio classification aimed at enhancing environmental awareness for deaf individuals. By leveraging convolutional neural networks (CNNs) and recurrent neural networks (RNNs), the system is trained to recognize a wide range of environmental sounds—such as sirens, alarms, doorbells, approaching vehicles, and human speech patterns. The classified audio cues are then translated into intuitive visual or haptic feedback through wearable or mobile devices. Experimental results demonstrate high accuracy in sound classification and low latency in alert delivery, making the system suitable for real-world deployment. This approach holds significant potential to bridge the sensory gap for the deaf community and foster greater independence and situational awareness. Keywords - CNN, RNN, Audio Classification, Deep Learning, Machine Learning