用数据验证吉他音色描述词的科学性，发现传统理论与实际感知不符。
Does it Chug? Towards a Data-Driven Understanding of Guitar Tone Description
- 通过调整音效生成多样音色，采集专家对音色形容词的标注数据。
- 发现'温暖''厚重'等形容词与频谱特征的关联不一致，存在反例。
- 适合音乐技术、声学感知或人机交互研究者参考。
自然语言常用于描述乐器音色，如“温暖”或“厚重”。由于这些描述基于人类感知，不同人对特定形容词对应的声学特征可能存在分歧。本文提出一种数据驱动方法，深入理解吉他音色描述词。主要贡献是一个音色形容词数据集，通过调整均衡器（EQ）和失真等效果，处理单段音频生成多样化音色，并通过众包方式让专家完成成对比较和标签任务获取形容词标注。分析该数据集后，揭示了形容词评分与声学特征之间的相关性，同时发现若干与现有频谱特征理论相悖的现象，表明需建立更精细、基于数据的音色理解框架。
原文摘要 · Abstract (English)
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.
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