In music information retrieval, a huge search space has to be explored because a query audio clip can start at any position of any music in the database, and also a query is often corrupted by highly significant noise and distortion. Audio fingerprints have attracted much attention recently for providing compact representation of the perceptually relevant parts of audio signals. In this paper, we propose an extremely fast method of exploring a huge hamming space for audio fingerprinting systems. The effectiveness of our method has been evaluated by experiments using databases of 8,740 real songs and 800 artificially corrupted and 268 real queries.
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