张力音乐:一种自动预测模型,用于音乐紧张的预测
Alice Vivien Barchet1, Johanna M Rimmele1,2, Claire Pelofi2,3
1Department of Cognitive Neuropsychology, Max Planck Institute for Empirical Aesthetics, Frankfurt, Germany.
PloS one
|January 19, 2024
概括
本研究介绍了TenseMusic,这是一个开源工具,可以自动预测音频中的音乐紧张. 这个模型使用关键的音乐特征准确预测听众的紧张感知,推进音乐认知研究.
科学领域:
- 音乐认知 音乐认知
- 计算音乐学 计算音乐学
- 精神声学是一种精神声学.
背景情况:
- 音乐的紧张和释放对于音乐的听力体验至关重要.
- 以前用于预测音乐紧张的模型缺乏自动特征提取,限制了可访问性.
- 听众对紧张的感知是从音乐特征中一致和可预测的.
研究的目的:
- 开发一个开源的,仅使用音频输入的音乐张力自动预测模型.
- 为使研究人员能够广泛使用音乐紧张的计算建模.
- 研究客观音乐特征与主观听众对紧张感知之间的关系.
主要方法:
- 开发了TenseMusic,这是一个自动预测张力模型.
- 使用最先进的音乐信息检索 (MIR) 方法来提取功能.
- 提取了六个关键特征:大声度,音高,音调张力,粗度,节奏和开始频率.
- 使用拉索回归对38首西方古典音乐作品的行为紧张评级进行了优化.
主要成果:
- TenseMusic自动从音频中提取相关的音乐特征.
- 该模型在预测持续的行为紧张等级方面取得了很高的准确性.
- 预测和实际的张力评级之间的平均相关性近似r = .60跨音乐作品.
结论:
- TenseMusic提供了一个经过验证的,开源的工具,用于预测音乐紧张.
- 这种工具可以促进进一步研究音乐认知和紧张的神经相关性.
- 该模型的成功表明客观的音乐特征可以可靠地预测主观的紧张感知.
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