Note Onset Deviations as Musical Piece Signatures

A competent interpretation of a musical composition presents several non-explicit departures from the written score. Timing variations are perhaps the most important ones: they are fundamental for expressive performance and a key ingredient for conferring a human-like quality to machine-based music renditions. However, the nature of such variations is still an open research question, with diverse theories that indicate a multi-dimensional phenomenon. In the present study, we consider event-shift timing variations and show that sequences of note onset deviations are robust and reliable predictors of the musical piece being played, irrespective of the performer. In fact, our results suggest that only a few consecutive onset deviations are already enough to identify a musical composition with statistically significant accuracy. We consider a mid-size collection of commercial recordings of classical guitar pieces and follow a quantitative approach based on the combination of standard statistical tools and machine learning techniques with the semi-automatic estimation of onset deviations. Besides the reported results, we believe that the considered materials and the methodology followed widen the testing ground for studying musical timing and could open new perspectives in related research fields.

对音乐作品的专业演绎，往往存在若干与书面乐谱非显式的背离之处。其中演奏时序变化（timing variations）或许是最为关键的一类：此类变化对于实现富有表现力的演奏至关重要，同时也是赋予机器音乐演奏以类人质感的核心要素。然而，此类时序变化的本质仍是一个待解的研究课题，现有多种理论均表明其属于多维度现象。本研究聚焦于事件偏移类时序变化（event-shift timing variations），研究表明，音符起奏偏差（note onset deviations）序列可作为被演奏乐曲的稳健且可靠的预测因子，且不受演奏者个体差异影响。事实上，本研究结果显示，仅需少量连续的起奏偏差序列，即可达到具有统计学显著性的准确率以识别目标乐曲。本研究采用中等规模的古典吉他作品商业录音集作为研究素材，并采用结合了标准统计工具与机器学习技术、辅以半自动化起奏偏差估算的定量研究方法。除上述研究结果外，本研究采用的研究素材与方法，有望拓展音乐演奏时序研究的试验范畴，并为相关研究领域开辟全新视角。