Raga Agnostic Automatic Melodic Accompaniment for Alapana in Carnatic Music

Alapana in Carnatic music is a popular form of improvisation where the musicians elevate the mood of a raga (akin to modes/scales in Western music) without any percussive elements. Melodic accompanists typically support in unison by aptly playing selected parts of the phrases sung by the lead artist. This work establishes an algorithm for raga agnostic automatic melodic accompaniment for raga alapana in Carnatic music. Since the choice of raga is not known prior to the performance, it is essential to have raga-agnosticism for the algorithm. We propose a sub-phrase informed offline algorithm as well as a causal real-time implementation. We evaluated our approach by comparing it to established baselines for 4 randomly chosen ragas and expert listening tests assert our approach is significantly better than the baseline approaches and is comparable to a human accompanist.

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Publication Details

Published
2025-04-06
DOI
https://doi.org/10.1109/icasspw65056.2025.11011219
Primary Topic
Music and Audio Processing
Type
article
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article

Raga Agnostic Automatic Melodic Accompaniment for Alapana in Carnatic Music

Raghavasimhan Sankaranarayanan, Gil Weinberg
Music and Audio Processing
article

Raga Agnostic Automatic Melodic Accompaniment for Alapana in Carnatic Music

Raghavasimhan Sankaranarayanan, Gil Weinberg
article en

Abstract

Alapana in Carnatic music is a popular form of improvisation where the musicians elevate the mood of a raga (akin to modes/scales in Western music) without any percussive elements. Melodic accompanists typically support in unison by aptly playing selected parts of the phrases sung by the lead artist. This work establishes an algorithm for raga agnostic automatic melodic accompaniment for raga alapana in Carnatic music. Since the choice of raga is not known prior to the performance, it is essential to have raga-agnosticism for the algorithm. We propose a sub-phrase informed offline algorithm as well as a causal real-time implementation. We evaluated our approach by comparing it to established baselines for 4 randomly chosen ragas and expert listening tests assert our approach is significantly better than the baseline approaches and is comparable to a human accompanist.

Georgia Institute of Technology (US)
Openalex Percentile: Top 87%
Music and Audio Processing
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