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Article

Gulati, S., Bellur, A., Salamon, J., Ranjani, H. G., Ishwar, V., Murthy, H. A., & Serra, X. (2014). Automatic tonic identification in Indian art music: approaches and evaluation. Journal of New Music Research, 43(1), 53–71.

[Published Document] [Postprint manuscript (PDF@MTG)] [BibTex]

Abstract

The tonic is a fundamental concept in Indian art music. It is the base pitch, which an artist chooses in order to construct the melodies during a rāg(a) rendition, and all accompanying instruments are tuned using the tonic pitch. Consequently, tonic identiﬁcation is a fundamental task for most computational analyses of Indian art music, such as intonation analysis, melodic motif analysis and rāg recognition. In this paper we review existing approaches for tonic identiﬁcation in Indian art music and evaluate them on six diverse datasets for a thorough comparison and analysis. We study the performance of each method in diﬀerent contexts such as the presence/absence of additional metadata, the quality of audio data, the duration of audio data, music tradition (Hindustani/Carnatic) and the gender of the singer (male/female). We show that the approaches that combine multi-pitch analysis with machine learning provide the best performance in most cases (90% identiﬁcation accuracy on an average), and are robust across the aforementioned contexts compared to the approaches based on expert knowledge. In addition, we also show that the performance of the later can be improved when additional metadata is available to further constrain the problem. Finally, we present a detailed error analysis of each method, providing further insights into the advantages and limitations of the methods.

Code

Tonic pitch for a recording can be compute using the code described here. Note that this is not the code used in the experimentation, although the results should be similar. The original experiments were conducted in MATLAB.