Filtering methods for content-based retrieval on indexed symbolic music databases
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We introduce fast filtering methods for content-based music retrieval problems, where the music is modeled as sets of points in the Euclidean plane, formed by the (on-set time, pitch) pairs. The filters exploit a precomputed index for the database, and run in time dependent on the query length and intermediate output sizes of the filters, being almost independent of the database size. With a quadratic size index, the filters are provably lossless for general point sets of this kind. In the context of music, the search space can be narrowed down, which enables the use of a linear sized index for effective and efficient lossless filtering. For the checking phase, which dominates the overall running time, we exploit previously designed algorithms suitable for local checking. In our experiments on a music database, our best filter-based methods performed several orders of a magnitude faster than the previously designed solutions.
KeywordsContent-based retrieval Symbolically encoded polyphonic music Filtering Indexing
We thank MSc Ben Sach from University of Bristol and Dr. Kimmo Fredriksson from University of Kuopio for providing us their implementations of the MsM and Fg6 algorithms, respectively.
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