A REAL-TIME ENVIRONMENTAL SOUND RECOGNITION SYSTEM FOR THE ANDROID OS

Sounds around us convey the context of daily life activities. There are 360 million individuals [1] worldwide who experience some form of deafness. For them, missing these contexts such as fire alarm can not only be inconvenient but also life threatening. In this paper, we explore a combination of different audio feature extraction algorithms that would aid in increasing the accuracy of identifying environmental sounds and also reduce power consumption. We also design a simple approach that alleviates some of the privacy concerns, and evaluate the implemented real-time environmental sound recognition system on Android mobile devices. Our solution works in embedded mode where sound processing and recognition are performed directly on a mobile device in a way that conserves battery power. Sound signals were detected using standard deviation of normalized power sequences. Multiple feature extraction techniques like zero crossing rate, Mel-frequency cepstral coefficient (MFCC), spectral flatness, and spectral centroid were applied on the raw sound signal. Multi-layer perceptron classifier was used to identify the sound. Experimental results show improvement over state-of-the-art.