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1. D. Ellis, “Detecting alarm sounds,” Recognition and Organization of Real-World Sounds: Workshop on Consistent and Reliable Acoustic Cues, Aalborg, Denmark (2001), pp. 5962.
2. A.S. Bregman, Auditory Scene Analysis: The Perceptual Organization of Sound (MIT Press, Cambridge, MA, 1990), pp. 1773.
3. X. Xiao, H. Yao, and C. Guo, “Automatic detection of alarm sounds in cockpit voice recordings,” Proceedings of the 2009 IITA International Conference on Control, Automation and Systems Engineering (2009), pp. 599602.

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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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