Abstract:

The current query by humming system can hardly be extended to large massive database as most of them adopt the features extracted from MIDI files, which are not widely used, and the very time-consuming matching methods. Because the SPRING algorithm dramatically reduces the algorithm complexity of subsequence matching, we regard query by humming as a subsequence similarity matching problem and exploit the SPRING algorithm as the core matching method to compare the melody feature extracted from polyphonic music, reducing the matching time greatly. Furthermore, accelerated by GPU, the SPRING algorithm achieves a near 40 times speedup over the serial version. The processing ability per node can reach thousands of sequences matching per second under down sampling. With the help of clusters, the processing scale can be extended heavily, which shows that our system has a good scalability. At the same time, the accuracy results of query by humming point out the encountered problems and the future direction.

Key words: query by humming;feature extraction;SPRING;GPU;MPI