What makes a piece of music appear complex to a listener? This research extends previous work by Eerola (2016), examining information content generated by a computational model of auditory expectation (IDyOM) based on statistical learning and probabilistic prediction as an empirical definition of perceived musical complexity. We systematically manipulated the melody, rhythm, and harmony of short polyphonic musical excerpts using the model to ensure that these manipulations systematically varied information content in the intended direction. Complexity ratings collected from 28 participants were found to positively correlate most strongly with melodic and harmonic information content, which corresponded to descriptive musical features such as the proportion of out-of-key notes and tonal ambiguity. When individual differences were considered, these explained more variance than the manipulated predictors. Musical background was not a significant predictor of complexity ratings. The results support information content, as implemented by IDyOM, as an information-theoretic measure of complexity as well as extending IDyOM9s range of applications to perceived complexity.

中文翻译：

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音乐复杂性的信息理论建模

是什么使音乐对听众显得复杂？这项研究扩展了Eerola（2016）的先前工作，研究了基于统计学习和概率预测的听觉期望计算模型（IDyOM）生成的信息内容，作为对感知音乐复杂性的经验定义。我们使用该模型来系统地操纵短复音音乐摘录的旋律，节奏和和声，以确保这些操纵能在预期的方向上系统地改变信息内容。发现从28名参与者那里收集的复杂度等级与旋律和和声信息内容呈最强的正相关，这与描述性的音乐特征相对应，例如音调比例和音调含糊。考虑个人差异时，这些比解释的预测因子解释了更多的方差。音乐背景并不是复杂度等级的重要预测指标。结果支持IDyOM实施的信息内容，作为信息理论上对复杂性的度量，并将IDyOM9的应用范围扩展到感知到的复杂性。