Dual-microphone Sounds of Daily Life classification for telemonitoring in a noisy environment

Telemonitoring of elderly people in their homes using video cameras is complicated by privacy concerns, and hence sound has emerged as a promising alternative that is more acceptable to users. We investigate methods to address the accuracy degradation of sound classification that arises in the presence of background noise typical of a practical telemonitoring situation. A dual microphone configuration is used to record a database of Sounds of Daily Life (SDL) in a kitchen. We introduce a new algorithm employing the eigenvalues of the cross-spectral matrix of the recorded signals to detect the endpoints of a SDL in the presence of background noise. Independent component analysis is also used to improve the signal to noise ratio of the SDL. Results on a 7-class noisy SDL classification problem show that the error rate the proposed SDL classification system can be improved by up to 97% relative to a single-microphone system without noise reduction techniques, when evaluated on a large SDL database with SNRs in the range 0–28 dB.