In this paper we propose a music Query by Humming System made of two main functional blocks; the first implements a voice-to-midi transcription algorithm to process the query, the second implements a search engine based on a novel template matching technique for Dynamic Time Warping. The voice-to-midi algorithm transforms the sung or hummed query in a MIDI file by segmenting and identifying the notes' sequence. The search engine uses a Template Matching technique to produce a list of possible melodies that best match the searched one. In the test phase, first, we investigated performance of the search engine in retrieval using a synthetic test bench; a set of artificial queries is build placing and adjusting, in the queries, patterns of typical disturbance. Second, we use a genetic algorithm to automatically optimize the performance of the overall system using a real-life test bench. Results highlight that the proposed MIR system has good performances and is robust enough to be employed in real life applications. © 2010 IEEE.
A query by humming system for music information retrieval / Mario, Antonelli; Rizzi, Antonello; DEL VESCOVO, Guido. - STAMPA. - (2010), pp. 586-591. (Intervento presentato al convegno 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10 tenutosi a Cairo; Egypt nel 29 November 2010 through 1 December 2010) [10.1109/isda.2010.5687200].
A query by humming system for music information retrieval
RIZZI, Antonello;DEL VESCOVO, Guido
2010
Abstract
In this paper we propose a music Query by Humming System made of two main functional blocks; the first implements a voice-to-midi transcription algorithm to process the query, the second implements a search engine based on a novel template matching technique for Dynamic Time Warping. The voice-to-midi algorithm transforms the sung or hummed query in a MIDI file by segmenting and identifying the notes' sequence. The search engine uses a Template Matching technique to produce a list of possible melodies that best match the searched one. In the test phase, first, we investigated performance of the search engine in retrieval using a synthetic test bench; a set of artificial queries is build placing and adjusting, in the queries, patterns of typical disturbance. Second, we use a genetic algorithm to automatically optimize the performance of the overall system using a real-life test bench. Results highlight that the proposed MIR system has good performances and is robust enough to be employed in real life applications. © 2010 IEEE.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.