Information-Theoretic Measures Predict the Human Judgment of Rhythm Complexity
This paper examines the assumption that we continuously while listening tend to focus on the most complex (least repetitive) voice, experiencing this as foreground. We present a computational model calculating the level of attraction a voice in a score is likely to require at a given time. The model is based on a music information complexity measure. Calculating the complexity in each voice over a short time window, the model predicts the most complex voice to be the most interesting to listen to. The capability of the model is evaluated in terms of melody prediction. With promising results the predicted notes are compared to melody annotated scores. We discuss how to measure music complexity of pitch and rhythm, and examine which factors are the most important in the perception of music.