用复杂网络分析古大提琴音高波动，揭示演奏风格与频率规律的关联
Complexity of frequency fluctuations and the interpretive style in the bass viola da gamba
- 将音乐信号转为声学复杂网络，通过频谱分解建模音高波动
- 发现频率波动服从特定统计分布，中心性指标识别核心音组
- 揭示同一乐手不同曲目中相似频率特征的深层规律，适合音乐信息检索研究者
通过对一组乐曲的音频信号建模为复杂网络，研究古大提琴演奏中音高波动复杂性与诠释风格的关系。结合跨学科科学与音乐方法，对频谱进行分解，并将频率成分转化为声学网络。通过最优拟合分析确定描述频率行为的统计分布，计算中心性度量并识别团簇，以刻画该网络特性。结果表明，频率波动存在特定的统计规律；中心性度量确认了乐曲中最具影响力且稳定的音组，最大团簇的识别则揭示了密切互动的功能音组，这些音组共同促成复杂频率波动的涌现。因此，通过将声音建模为复杂网络，可明确将大规模统计规律与同一演奏者在不同音乐事件中出现的相似频率波动相关联。
原文摘要 · Abstract (English)
Audio signals in a set of musical pieces are modeled as a complex network for studying the relationship between the complexity of frequency fluctuations and the interpretive style of the bass viola da gamba. Based on interdisciplinary scientific and music approaches, we compute the spectral decomposition and translated its frequency components to a network of sounds. We applied a best fit analysis for identifying the statistical distributions that describe more precisely the behavior of such frequencies and computed the centrality measures and identify cliques for characterizing such a network. Findings suggested statistical regularities in the type of statistical distribution that best describes frequency fluctuations. The centrality measure confirmed the most influential and stable group of sounds in a piece of music, meanwhile the identification of the largest clique indicated functional groups of sounds that interact closely for identifying the emergence of complex frequency fluctuations. Therefore, by modeling the sound as a complex network, we can clearly associate the presence of large-scale statistical regularities with the presence of similar frequency fluctuations related to different musical events played by a same musician.
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