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Robust modeling of musical chord sequences using probabilistic N-grams

Ricardo Scholz 1 Emmanuel Vincent 1 Frédéric Bimbot 1 
1 METISS - Speech and sound data modeling and processing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : The modeling of music as a language is a core issue for a wide range of applications such as polyphonic music retrieval, automatic style identification, audio to symbolic music transcription and computer-assisted composition. In this paper, we focus on the modeling of chord sequences by probabilistic N-grams. Previous studies using these models have achieved limited success, due to overfitting and to the use of a single chord labeling scheme. We investigate these issues using model smoothing and selection techniques initially designed for spoken language modeling. This approach is evaluated over a set of songs by The Beatles, considering several chord labeling schemes. Initial results show that the accuracy of N-grams is increased but that additional improvements may still be achieved in the future using more advanced, possibly music-specific, smoothing techniques.
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Submitted on : Tuesday, December 7, 2010 - 2:12:02 PM
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  • HAL Id : inria-00544166, version 1


Ricardo Scholz, Emmanuel Vincent, Frédéric Bimbot. Robust modeling of musical chord sequences using probabilistic N-grams. 2009 IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), Apr 2009, Taipei, Taiwan. pp.53--56. ⟨inria-00544166⟩



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