Detrended Fluctuation Analysis of Music Signals: Danceability Estimation and Further Semantic Characterization
Detrended Fluctuation Analysis of Music Signals: Danceability Estimation and Further Semantic Characterization
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音乐信号的去趋势波动分析：可舞性估计和进一步的语义表征
DOI：
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发表时间：
2005
期刊：
影响因子：
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通讯作者：
P. Herrera
中科院分区：
文献类型：
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作者：
Sebastian Streich;P. Herrera
去趋势波动分析（DFA）已由Peng等人提出。[1]用于生物医学数据。它起源于分形分析，揭示了不同时间尺度上数据序列之间的相关性。Jennings等人。[2]使用DFA衍生的特征，去趋势方差波动指数，用于音乐流派分类，将该方法引入音乐分析领域。在本文中，我们进一步利用这种低层次的功能，语义音乐描述的关系。它是在7750首带有手动注释的语义标签（如“精力充沛”或“忧郁”）的曲目上计算的。我们发现，这些标签和这个功能之间的统计强关联支持的假设，它可以被链接到一个音乐属性，这可能被描述为“跳舞”。
Detrended fluctuation analysis (DFA) has been proposed by Peng et al. [1] to be used on biomedical data. It originates from fractal analysis and reveals correlations within data series across different time scales. Jennings et al. [2] used a DFA-derived feature, the detrended variance fluctuation exponent, for musical genre classification introducing the method to the music analysis field. In this paper we further exploit the relation of this low-level feature to semantic music descriptions. It was computed on 7750 tracks with manually annotated semantic labels like “Energetic” or “Melancholic”. We found statistically strong associations between some of these labels and this feature supporting the hypothesis that it can be linked to a musical attribute which might be described as “danceability”.