Streaming audio classification in Smart Home environments

In this research, we develop and integrate methods for real-time streaming audio classification based on psychoacoustic models of hearing as well as techniques in pattern recognition. Specifically, a framework for auditory event detection and signal description by means of computer vision approach has been designed to enable real-time processing and classification of audio signals present in home environments. Local binary patterns are employed to describe the extracted sound blobs in the spectrogram. Experimental results show that the proposed approach is quite effective, achieving an overall recognition rate of 80-90% for 8 types of audio input. The performance degrades only slightly in the presence of noise and other interferences.