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.
Maunder
David
D
School of Electrical Engineering and Telecommunications, University of New South Wales 2052 Australia. d.maunder@gmail.com
Ambikairajah
Eliathamby
E
Epps
Julien
J
Celler
Branko
B
D000203:Activities of Daily Living; D000465:Algorithms; D001185:Artificial Intelligence; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004779:Environment Design; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods; D018670:Monitoring, Ambulatory / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D012815:Signal Processing, Computer-Assisted; D013016:Sound; D013018:Sound Spectrography / Q000379:methods*; D017216:Telemedicine / Q000379:methods*