Automatic rāga recognition is one of the fundamental computational tasks in Indian art music. Motivated by the way seasoned listeners identify rāgas, we propose a rāga 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 rāga recognition. To evaluate our approach, we compile a music collection of over 124 hours, comprising 480 recordings and 40 rāgas. We obtain 70% accuracy with the full 40-rāga collection, and up to 92% accuracy with its 10-rāga subset. We show that phrase-based rāga 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 rāga recognition, is the identification of rāga-phrases. These phrases can be used as a dictionary of semantically-meaningful melodic units for several computational tasks in Indian art music.
To appear in ICASSP 2016: S. Gulati, J. Serrà, V. Ishwar, S. Senturk, & X. Serra. Phrase-based raga recognition using vector space modeling. Proc. of the IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP). In Press. [MTG]