Comunicació presentada a la 18th International Society for Music Information Retrieval Conference (ISMIR 2017), celebrada els dies 23 a 27 d'octubre de 2017 a Suzhou, Xina.

Automatic meter analysis aims to annotate a recording of a metered piece of music with its metrical structure. This analysis subsumes correct estimation of the type of meter, the tempo, and the alignment of the metrical structure with the music signal. Recently, Bayesian models have been successfully applied to several of meter analysis tasks, but depending on the musical context, meter analysis still poses significant challenges. In this paper, we investigate if there are benefits to automatic meter analysis from additional a priori information about the metrical structure of music. We explore informed automatic meter analysis, in which varying levels of prior information about the metrical structure of the music piece is available to analysis algorithms. We formulate different informed meter analysis tasks and discuss their practical applications, with a focus on Indian art music. We then adapt state of the art Bayesian meter analysis methods to these tasks and evaluate them on corpora of Indian art music. The experiments show that the use of additional information aids meter analysis and improves automatic meter analysis performance, with significant gains for analysis of downbeats.

This work is partly supported by the European Research Council as part of the CompMusic project (ERC grant agreement 267583). Ajay Srinivasamurthy is currently with Idiap Research Institute, Martigny, Switzerland.