Initial Investigation of a Notification System for Indoor Alarm Sounds Using a Neural Network

Abstract

Many devices can inform the user of everything from a visitor’s arrival at the door to a dangerous gas leak detection. For hearing-impaired people, there are some devices that can notify using light, etc., rather than by sound. However, these are individual devices and are relatively expensive due to their limited production volume. In this paper, a neural network was used as a method to classify alarm sounds of eight types of equipment. Two feature elements such as power spectrum and Mel Frequency Cepstrum Coefficients (MFCC) are taken as feature quantities to enter in this network, and its performance was evaluated. We implemented a neural network learned model on a Raspberry Pi and constructed a system that transmits classification results to a smartphone via Bluetooth. We generated 8 types of alarm sounds, plus indoor environmental sounds and speech sounds, for a total of ten kinds of sounds in the actual use environment of the classification experiment. This produced classification rates of 83.0% and 82.0% in experiments using learned models generated by power spectrum and MFCC. For the 8 alarm sounds, the classification rate was 87.5% by power spectrum and 77.5% by MFCC. It was confirmed that good performance could be obtained if power spectrum is used to determine feature elements in alarm sound classification.