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

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.

• Type: Conference
• Year: 2010
• Where: ICASSP
• Authors: Keansub Lee, Daniel P. W. Ellis, Alexander C. Loui
• Keywords: Acoustic Event, Consumer Video Archives, ICASSP 2010, Longer Soundtrack, Signal Processing