Detection and Classification of Various Listening Environments for Hearing-Impaired Individuals Using CRNN

This article describes a convolutional recurrent neural network (CRNN) model designed to improve the categorization of various listening situations for people with hearing impairments. As the demand for sophisticated audio analysis in smart city initiatives and supporting technologies grows, this CRNN model effectively combines Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) to extract Mel Frequency Cepstral Coefficients (MFCCs) and capture temporal relationships in audio data. This collaboration takes advantage of CNN s' skills in spotting spatial patterns and LSTMs' capabilities in handling with sequential information. The model executed admirably, with a training accuracy of 99.97% and a testing accuracy of 95.84%, suggesting strong generalization abilities across a variety of listening scenarios. This research highlights the effectiveness of CRNN in classifying complex audio data and suggests possible improvements including expanding the dataset, using advanced data augmentation techniques, and optimizing the model architecture. These strategies aim to overcome challenges in classifying complicated or overlapping listening situations.