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Detecting local semantic concepts in environmental sounds using Markov model based clustering

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3 Author(s)
Keansub Lee ; Dept. of Elec. Eng., Columbia Univ., New York, NY, USA ; Ellis, D.P.W. ; Loui, A.C.

Detecting the time of occurrence of an acoustic event (for instance, a cheer) embedded in a longer soundtrack is useful and important for applications such as search and retrieval in consumer video archives. We present a Markov-model based clustering algorithm able to identify and segment consistent sets of temporal frames into regions associated with different ground-truth labels, and simultaneously to exclude a set of uninformative frames shared in common from all clips. The labels are provided at the clip level, so this refinement of the time axis represents a variant of Multiple-Instance Learning (MIL). Evaluation shows that local concepts are effectively detected by this clustering technique based on coarse-scale labels, and that detection performance is significantly better than existing algorithms for classifying real-world consumer recordings.

Published in:

Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on

Date of Conference:

14-19 March 2010