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.

The rapid increase of storage capacity has brought along large-scale multimedia databases. To access such databases, content-based retrieval methods are needed in order to avoid the burden of handcraft involved in building a query system working on metadata. The burgeoning demand for such methods can be seen, for instance, in the number of researchers working on developing tools and algorithms to this end. In this paper, we present a prototypic, client-server query engine for content-based music retrieval (CBAAR). Our main aim is to help researchers working in the field so that they could have a retrieval platform where to embed and test their novel tools and algorithms without the burden of building a whole system from scratch. We give an overview to the platform: the architectural solutions, the communication protocols and user interface design. As for an example, we have embedded in this platform some music similarity and transcription algorithms developed in the C-BRAHMS research group, and thus achieved a complete retrieval system that can be queried on our website. We describe these algorithms in brief and discuss the performance of the retrieval system. The platform is released to public under the GNU General Public License, allowing anyone interested to freely use and modify the software.