Automated detection of alarm sounds | Zendy

Robert A. Lutfi | Zendy; Inseok Heo | Zendy

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Automated detection of alarm sounds

Author(s) -

Robert A. Lutfi,

Inseok Heo

Publication year - 2012

Publication title -

the journal of the acoustical society of america

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.619

H-Index - 187

eISSN - 1520-8524

pISSN - 0001-4966

DOI - 10.1121/1.4734555

Subject(s) - alarm , acoustics , computer science , autocorrelation , sound (geography) , noise (video) , speech recognition , artificial intelligence , mathematics , physics , electrical engineering , statistics , engineering , image (mathematics)

Two approaches to the automated detection of alarm sounds are compared, one based on a change in overall sound level (RMS), the other a change in periodicity, as given by the power of the normalized autocorrelation function (PNA). Receiver operating characteristics in each case were obtained for different exemplars of four classes of alarm sounds (bells/chimes, buzzers/beepers, horns/whistles, and sirens) embedded in four noise backgrounds (cafeteria, park, traffic, and music). The results suggest that PNA combined with RMS may be used to improve current alarm-sound alerting technologies for the hard-of-hearing.

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