Clap detection and discrimination for rhythm therapy

An auditory training system relies on determining how well individual users can clap their hands together ´in time´ with a prompt. Because the system is intended for a scenario in which an entire class of students is simultaneously engaged in this training, each system must distinguish between the claps of a single user and background claps from other nearby users. Available cues for this discrimination include the absolute energy of the clap sound, its source azimuth (estimated from stereo microphones), and its range as conveyed by the direct-to-reverberant energy balance. We present a set of features to capture these cues, and report our results on detecting and distinguishing ´near-field´ and ´far-field´ claps in a corpus of 1650 claps recorded in realistic classroom environments. When room and location are matched between training and test data, the classification error rate falls as low as 0.13%; when training data is recorded from a separate room, the error rate is still below 4.8% in the worst case.