Failure Detection from the Knocking Sounds Using Convolutional Neural Network

Toilet quality assurance is a crucial process in ensuring lavatories meet rigorous performance, durability, and hygiene standards. The current standard of Maximum Performance (MaP) testing faces challenges, leading researchers to explore innovative approaches such as sound source classification for quality assurance. This approach involves collecting a diverse dataset of lavatory sounds and extracting relevant acoustic features. Deep learning models, particularly convolutional neural networks (CNNs), are trained on these features to accurately classify sound sources. The trained models were evaluated and compared by considering metrics such as classification accuracy, computational complexity, and model parameters. This paper performs these tests and chooses the most effective model to enhance the quality assurance process for toilets. Incorporating sound source classification techniques has several benefits, including the optimization of testing processes, non-intrusive performance assessment, and efficient resource utilization through targeted testing and troubleshooting. By improving the standards of lavatory quality, this approach ensures enhanced performance, durability, and hygiene of lavatories.