Detection of alarm sounds in noisy environments

Abstract

Sirens and alarms play an important role in everyday life since they warn people of hazardous situations, even when these are out of sight. Automatic detection of this class of sounds can help hearing impaired or distracted people, e.g., on the road, and contribute to their independence and safety. In this paper, we present a technique for the detection of alarm sounds in noisy environments. The technique is not limited to particular alarms and can detect most electronically generated alerting sounds within 200 ms. We consider a set of acoustic features and use the ReliefF algorithm to select only the ones that best differentiate between alarms and other sounds. We use an SVM classifier as the detector. On the tested dataset, consisting of several dozen alarm sounds and several dozen background noises, the proposed technique shows an accuracy of 98% per audio frame. With a larger training dataset, this result is expected to substantially improve.