A music search engine based on semantic text-based query

Search and retrieval of songs from a large music repository usually relies on added meta-information (e.g., title, artist or musical genre); or on specific descriptors (e.g. mood); or on categorical music descriptors; none of which can specify the desired intensity. In this work, we propose an early example of semantic text-based music search engine. The semantic description takes into account emotional and non-emotional musical aspects. The method also includes a query-by-similarity search approach performed using semantic cues. We model both concepts and musical content in dimensional spaces that are suitable for carrying intensity information on the descriptors. We process the semantic query with a Natural Language parser to capture only the relevant words and qualifiers. We rely on Bayesian Decision theory to model concepts and songs as probability distributions. The resulted ranked list of songs are produced through a posterior probability model. A prototype of the system has been proposed to 53 subjects for evaluation, with good ratings on performance, usefulness and potential.