This paper presents first steps towards a simple, robust computational model of automatic melody identification. Based on results from music psychology that indicate a relationship between melodic complexity and a listener’s attention, we postulate a relationship between musical complexity and the probability of a musical line to be perceived as the melody. We introduce a simple measure of melodic complexity, present an algorithm for predicting the most likely melody note at any point in a piece, and show experimentally that this simple approach works surprisingly well in rather complex music. 1

Abstract. In this paper, we will examine the importance of music complexity as a factor for melody recognition in multi-voiced popular music. The assumption is that the melody (or lead instrument) will contain the largest amount of information – that it will be the least redundant voice. Measures of melodic complexity calculated from pitch and timing information are proposed. We test the different complexity measures and different prediction strategies, and evaluate them on the task of predicting which track of a MIDI file contains the main melody. Filtering out melody tracks can be useful when searching large databases for similar songs. 108 melody track annotated pop songs were included in the experiment. 1