Phrase-based rāga recognition using vector space modeling

Gulati, Sankalp; Serrà Julià, Joan; Ishwar, Vignesh; Sentürk, Sertan; Serra, Xavier

doi:http://dx.doi.org/10.1109/ICASSP.2016.7471638

Phrase-based rāga recognition using vector space modeling

Citation

Gulati S, Serrà J, Ishwar V, Sentürk S, Serra X. Phrase-based raga recognition using vector space modeling. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 2016 Mar 20-25; Shanghai, China. [New York]: IEEE; 2016. p. 66-70. DOI: 10.1109/ICASSP.2016.7471638

Abstract

Automatic raga recognition is one of the fundamental computational tasks in Indian art music. Motivated by the way seasoned listeners identify ragas, we propose a raga recognition approach based on melodic phrases. Firstly, we extract melodic patterns from a collection of audio recordings in an unsupervised way. Next, we group similar patterns by exploiting complex networks concepts and techniques. Drawing an analogy to topic modeling in text classification, we then represent audio recordings using a vector space model. Finally, we employ a number of classification strategies to build a predictive model for raga recognition. To evaluate our approach, we compile a music collection of over 124 hours, comprising 480 recordings and 40 ragas. We obtain 70% accuracy with the full 40-raga collection, and up to 92% accuracy with its 10-raga subset. We show that phrase-based raga recognition is a successful strategy, on par with the state of the art, and sometimes outperforms it. A by-product of our approach, which arguably is as important as the task of raga recognition, is the identification of raga-phrases. These phrases can be used as a dictionary of semantically-meaningful melodic units for several computational tasks in Indian art music.

Description

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.