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 IDyOM's range of applications to perceived complexity.
Information-theoretic Modeling of Perceived Musical Complexity
Author Sauvé composed the stimuli, collected, and analyzed the data and drafted the manuscript. Both authors conceptualized and designed the study and Pearce edited the manuscript. Sauvé was funded by a Queen Mary University College Studentship. These data are also published in Sauvé's PhD thesis, Chapter 7.
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Sarah A. Sauvé, Marcus T. Pearce; Information-theoretic Modeling of Perceived Musical Complexity. Music Perception 1 December 2019; 37 (2): 165–178. doi: https://doi.org/10.1525/mp.2019.37.2.165
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