Does it Chug? Towards a Data-Driven Understanding of Guitar Tone Description

TL;DR

This paper introduces a dataset of guitar tone clips with adjective annotations obtained via crowdsourcing, revealing complex correlations between spectral features and perceived timbre, challenging existing theories.

Contribution

It provides a novel dataset of guitar timbre with expert annotations and demonstrates the complexity of linking spectral features to perceived adjectives.

Findings

Correlations between adjective ratings and spectral features are complex.

Some data contradicts existing theories on timbral adjectives.

Highlights the need for a data-driven approach to understanding timbre.

Abstract

Natural language is commonly used to describe instrument timbre, such as a "warm" or "heavy" sound. As these descriptors are based on human perception, there can be disagreement over which acoustic features correspond to a given adjective. In this work, we pursue a data-driven approach to further our understanding of such adjectives in the context of guitar tone. Our main contribution is a dataset of timbre adjectives, constructed by processing single clips of instrument audio to produce varied timbres through adjustments in EQ and effects such as distortion. Adjective annotations are obtained for each clip by crowdsourcing experts to complete a pairwise comparison and a labeling task. We examine the dataset and reveal correlations between adjective ratings and highlight instances where the data contradicts prevailing theories on spectral features and timbral adjectives, suggesting a need for a more nuanced, data-driven understanding of timbre.