Scalable Discovery of Audio Fingerprint Motifs in Broadcast Streams With Determinantal Point Process Based Motif Clustering
Haotian Xu, Zhijian Ou · IEEE/ACM Transactions on Audio Speech and Language Processing · 2016
In this paper, we study the scalable discovery of audio repetitive patterns/motifs in long broadcast streams, where two segments are said to be repetitive if their audio fingerprints are close to each other. In this task, as we are confined to handle limited variability, we can adapt an audio hashing technique, originally proposed for searching a given music clip in music tracks, to successfully devise a linear complexity similarity matching method with a new step of repeated interval formation. This is the first contribution of this paper. As the similarity matching is super fast and thus coarse, there are false alarms in the large number of pairwise matches generated, which constitute a major source of noise. We propose applying subset selection to the original set of pairwise matches based on determinantal point processes (DPPs), as a filtering step, to reduce the noise. The selected subset of pairwise matches is then subjected to motif clustering. We successfully apply DPP-based subset selection to improve motif clustering, which has a nice property that favors both quality and diversity. This is the second contribution of this paper. The proposed method is thoroughly evaluated on a 9-hour real-world audio stream and is compared with several reference methods. The bootstrap technique is used for the significance test. It is shown that the similarity matching is computationally very efficient (above 100 times faster than real time), and the filtering step with DPPs can significantly improve the precision of motif discovery, without sacrificing the recall performance.