A new deep neural network and mobile application have been developed to assist the deaf and hard-of-hearing community by classifying and detecting five significant sounds: a running faucet, a dripping faucet, a car engine, a car horn, and a fridge alarm. This research builds on prior work that utilized a long short-term memory (LSTM) model and an enhanced self-captured dataset but did not produce a mobile application. The current study introduced a “negative” class to include irrelevant sounds encountered in real-world settings. The refined model achieved an area under the curve (AUC) score of 0.97, balancing precision and recall effectively for critical and benign sounds. A novel approach was taken to fine-tune the YamNet audio classification model, employing convolutional layers to minimize performance overhead for real-time mobile use. The model has been deployed on the Web through TensorFlow.js and is available as a Progressive Web App for offline access. Future research will focus on user testing to ensure the application and model meet the needs of its intended users.