A query by humming system based on locality sensitive hashing indexes
Signal Processing
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Query by humming (QBH) is a technique that is used for content-based music information retrieval. It is a challenging unsolved problem due to humming errors. In this paper a novel retrieval method called note-based locality sensitive hashing (NLSH) is presented and it is combined with pitch-based locality sensitive hashing (PLSH) to screen candidate fragments. The method extracts PLSH and NLSH vectors from the database to construct two indexes. In the phase of retrieval, it automatically extracts vectors similar to the index construction and searches the indexes to obtain a list of candidates. Then recursive alignment (RA) is executed on these surviving candidates. Experiments are conducted on a database of 5,000 MIDI files with the 2010 MIREX-QBH query corpus. The results show by using the combination approach the relatively improvements of mean reciprocal rank are 29.7% (humming from anywhere) and 23.8% (humming from beginning), respectively, compared with the current state-of-the-art method.