Beat Detection and Automatic Annotation of the Music of Bharatanatyam Dance using Speech Recognition Techniques

TL;DR

This paper presents a novel speech processing approach to automatically recognize and annotate the rhythmic structure of Bharatanatyam dance music, achieving high accuracy in bol and Sollukattu recognition, tempo estimation, and beat marking.

Contribution

It introduces the first comprehensive method using speech techniques for structural analysis and beat annotation of Bharatanatyam Sollukattu music.

Findings

85% bol recognition accuracy

95% Sollukattu recognition accuracy

96% tempo period estimation accuracy

Abstract

Bharatanatyam, an Indian Classical Dance form, represents the rich cultural heritage of India. Analysis and recognition of such dance forms are critical for the preservation of cultural heritage. Like in most dance forms, a Bharatanatyam dancer performs in synchronization with structured rhythmic music, called Sollukattu, which comprises instrumental beats and vocalized utterances (bols) to create a rhythmic music structure. Computer analysis of Bharatanatyam, therefore, requires a structural analysis of Sollukattus. In this paper, we use speech processing techniques to recognize bols. Exploiting the predefined structures of Sollukattus and the detected bols, we recognize the Sollukattu. We estimate the tempo period by two methods. Finally, we generate a complete annotation of the audio signal by beat marking. For this, we also use the information of beats detected from the onset envelope of a Sollukattu signal. For training and test, we create a data set for Sollukattus and annotate them. We achieve 85% accuracy in bol recognition, 95% in Sollukattu recognition, 96% in tempo period estimation, and over 90% in beat marking. This is the maiden attempt to fully structurally analyze the music of an Indian Classical Dance form and the use of speech processing techniques for beat marking.