Content-based audio copy detection using nearest-neighbor mapping

We report results on audio copy detection for TRECVID 2008 copy detection task. This task involves searching for transformed audio queries in over 200 hours of test audio. The queries were transformed in seven different ways, three of them involved mixing unrelated speech to the original query, making it a much more difficult task. We give results with a few promising algorithms and show that mapping each test frame to the nearest query frame results in robust audio copy detection. The minimal normalized detection cost rate (NDCR) for even the worst case transformations is less than 0.03. The algorithm provides easy parallel processing on a graphics processing unit, leading to a very fast search.