Sequential Pitch Distributions for Raga Detection

TL;DR

This paper introduces Sequential Pitch Distributions (SPD), a novel feature capturing temporal pitch patterns to improve raga detection accuracy in Indian classical music, achieving state-of-the-art results.

Contribution

It presents SPD as a new method for modeling temporal melodic information, significantly enhancing raga detection performance over traditional pitch distribution techniques.

Findings

Achieved 99% accuracy on Hindustani raga data

Achieved 88.13% accuracy on Carnatic raga data

SPD outperforms standard pitch distribution methods

Abstract

Raga is a fundamental melodic concept in Indian Art Music (IAM). It is characterized by complex patterns. All performances and compositions are based on the raga framework. Raga and tonic detection have been a long-standing research problem in the field of Music Information Retrieval. In this paper, we attempt to detect the raga using a novel feature to extract sequential or temporal information from an audio sample. We call these Sequential Pitch Distributions (SPD), which are distributions taken over pitch values between two given pitch values over time. We also achieve state-of-the-art results on both Hindustani and Carnatic music raga data sets with an accuracy of 99% and 88.13%, respectively. SPD gives a great boost in accuracy over a standard pitch distribution. The main goal of this paper, however, is to present an alternative approach to modeling the temporal aspects of the melody and thereby deducing the raga.