Does it Chug? Towards a Data-Driven Understanding of Guitar Tone Description
This work addresses the challenge of understanding subjective timbre descriptions for musicians and audio engineers, but it is incremental as it focuses on dataset creation and preliminary analysis.
The authors tackled the problem of mapping natural language adjectives like 'warm' or 'heavy' to acoustic features in guitar tone by creating a dataset through crowdsourced annotations of processed audio clips, revealing correlations and contradictions with existing theories.
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.