# Understanding and Classifying Cultural Music Using Melodic Features Case Of Hindustani, Carnatic And Turkish Music

## TL;DR

This paper develops a melody-based classification method for distinguishing Hindustani, Carnatic, and Turkish music styles using pitch and energy features, validated through listening tests and statistical analysis.

## Contribution

It introduces a novel set of melodic features derived from musicology to classify musical styles, emphasizing the role of melodic contour and energy variations.

## Key findings

- High correlation between automatic and subjective style labels
- Melody features alone can distinguish styles effectively
- Combining timbre and melody features improves classification accuracy

## Abstract

We present a melody based classification of musical styles by exploiting the pitch and energy based characteristics derived from the audio signal. Three prominent musical styles were chosen which have improvisation as integral part with similar melodic principles, theme, and structure of concerts namely, Hindustani, Carnatic and Turkish music. Listeners of one or more of these genres can discriminate between these based on the melodic contour alone. Listening tests were carried out using melodic attributes alone, on similar melodic pieces with respect to raga/makam, and removing any instrumentation cue to validate our hypothesis that style distinction is evident in the melody. Our method is based on finding a set of highly discriminatory features, derived from musicology, to capture distinct characteristics of the melodic contour. Behavior in terms of transitions of the pitch contour, the presence of micro-tonal notes and the nature of variations in the vocal energy are exploited. The automatically classified style labels are found to correlate well with subjective listening judgments. This was verified by using statistical tests to compare the labels from subjective and objective judgments. The melody based features, when combined with timbre based features, were seen to improve the classification performance.