2026-04-14T02:39:41Zhttps://recercat.cat/oai/request

oai:recercat.cat:10230/328782025-12-19T23:01:51Zcom_2072_6col_2072_452952

00925njm 22002777a 4500 dc Gulati, Sankalp author Serrà Julià, Joan author Ishwar, Vignesh author Serra, Xavier author 2017-10-09T09:51:03Z 2017-10-09T09:51:03Z 2016 Comunicació presentada a la 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), celebrada els dies 20 a 25 de març a Xangai, Xina. Ra̅ga motifs are the main building blocks of the melodic structures in Indian art music. Therefore, the discovery and characterization of such motifs is fundamental for the computational analysis of this music. We propose an approach for discovering ra̅ga motifs from audio music collections. First, we extract melodic patterns from a collection of 44 hours of audio comprising 160 recordings belonging to 10 ra̅gas. Next, we characterize these patterns by performing a network analysis, detecting non-overlapping communities, and exploiting the topological properties of the network to determine a similarity threshold. With that, we select a number of motif candidates that are representative of a ra̅ga, the ra̅ga motifs. For a formal evaluation we perform listening tests with 10 professional musicians. The results indicate that, on an average, the selected melodic phrases correspond to ra̅ga motifs with 85% positive ratings. This opens up the possibilities for many musically-meaningful computational tasks in Indian art music, including human-interpretable ra̅ga recognition, semantic-based music discovery, or pedagogical tools. This work is partly supported by the European Research Council under the European Unions Seventh Framework Program, as part of the CompMusic project (ERC grant agreement 267583). Carnatic music Rāga motif Melodic phrases Melodic similarity Indian art music Discovering rāga motifs by characterizing communities in networks of melodic patterns