IIIA-CSIC, Artificial Intelligence Research Institute, Spanish National Research Council, Bellaterra, Barcelona, Spain.
PLoS One. 2013 Jul 31;8(7):e69268. doi: 10.1371/journal.pone.0069268. Print 2013.
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.
对音乐作品的准确诠释往往需要对乐谱进行一些非显性的改动。其中，时值变化可能是最重要的改动：它们是富有表现力的演奏的基础，也是将基于机器的音乐演奏赋予人性化品质的关键要素。然而，这种变化的性质仍然是一个悬而未决的研究问题，不同的理论表明这是一个多维度的现象。在本研究中，我们考虑了时值移位的时值变化，并表明音高起音偏差序列是演奏乐曲的稳健且可靠的预测指标，与演奏者无关。事实上，我们的结果表明，仅通过几个连续的起音偏差就足以以具有统计学意义的准确度来识别一首乐曲。我们考虑了一个中等规模的古典吉他作品商业录音集，并采用了一种基于标准统计工具和机器学习技术的组合以及音高起音偏差的半自动估计的定量方法。除了报告的结果外，我们认为所考虑的材料和所遵循的方法拓宽了研究音乐时值的测试范围，并为相关研究领域开辟了新的视角。
