Drum Loops Retrieval from Spoken Queries

摘要

Recent efforts in audio indexing and music information retrieval mostly focus on melody. If this is appropriate for polyphonic music signals, specific approaches are needed for systems dealing with percussive audio signals such as those produced by drums, tabla or djembé. In this article, we present a complete system allowing the management of a drum patterns (or drumloops) database. Queries in this database are formulated with spoken onomatopoeias—short meaningless words imitating the different sounds of the drumkit. The transcription task necessary to index the database is performed using Hidden Markov Models (HMM) and Support Vector Machines (SVM) and achieves a 86.4% correct recognition rate. The syllables of spoken queries are recognized and a relevant statistical model allows the comparison and alignment of the query with the rythmic sequences stored in the database, in order to provide a set of the most relevant drum loops.