Identifying Ragas in Indian Music

In this work, we propose a method to identify the ragas of an Indian Carnatic music signal. This has several interesting applications in digital music indexing, recommendation and retrieval. However, this problem is hard due to (i) the absence of a fixed frequency for a note (ii) relative scale of notes (iii) oscillations around a note, and (iv) improvisations. In this work, we attempt the raga classification problem in a non-linear SVM framework using a combination of two kernels that represent the similarities of a music signal using two different features-pitch-class profile and n-gram distribution of notes. This differs from the previous pitch-class profile based approaches where the temporal information of notes is ignored. We evaluated the proposed approach on our own raga dataset and Comp Music dataset and show an improvement of 10.19% by combining the information from two features relevant to Indian Carnatic music.