Clustering of Indonesian and Western Gamelan Orchestras through Machine Learning of Performance Parameters

TL;DR

This study uses machine learning to analyze performance parameters of Indonesian and Western gamelan orchestras, revealing distinct clustering based on timbre and articulation, and highlighting differences in performance style and variability.

Contribution

It applies self-organizing maps to classify gamelan ensembles by performance features, uncovering stylistic differences and similarities in tonal systems and articulation.

Findings

Western ensembles show reduced articulation and form variability.

Clustering based on timbre features separates Indonesian and Western groups.

Tonal system clustering does not distinguish between the two traditions.

Abstract

Indonesian and Western gamelan ensembles are investigated with respect to performance differences. Thereby, the often exotistic history of this music in the West might be reflected in contemporary tonal system, articulation, or large-scale form differences. Analyzing recordings of four Western and five Indonesian orchestras with respect to tonal systems and timbre features and using self-organizing Kohonen map (SOM) as a machine learning algorithm, a clear clustering between Indonesian and Western ensembles appears using certain psychoacoustic features. These point to a reduced articulation and large-scale form variability of Western ensembles compared to Indonesian ones. The SOM also clusters the ensembles with respect to their tonal systems, but no clusters between Indonesian and Western ensembles can be found in this respect. Therefore, a clear analogy between lower articulatory variability and large-scale form variation and a more exostistic, mediative and calm performance expectation and reception of gamelan in the West therefore appears.