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. Retrieving music from large digital databases is a demanding computational task. The cost for indexing and searching depends not only on the computational effort of measuring musical similarity, but also heavily on the number and sizes of files in the database. One way to speed up music retrieval is to reduce the search space by removing redundant and uninteresting material in the database. We propose a simple measure of ‘interestingness ’ based on music complexity, and present a reduction algorithm for MIDI files based on this measure. It is evaluated by comparing reduction ratios and the correctness of retrieval results for a query by humming task before and after applying the reduction. 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

This paper explores one way to use music in the context of affective design. We've made a real-time music generator that is designed around the concepts of valence and arousal, which are two components of certain models of emotion. When set to a desired valence and arousal, the algorithm plays music corresponding to the intersection of these two parameters. We designed our algorithm using psychological theory of emotion and parametrized features of music which have been tested for affect. The results are a modular algorithm design, in which our parameters can be implemented in other affective music algorithms. We describe our implementation of these parameters, and our strategy for manipulating the parameters to generate musical emotion. Finally we discuss possible applications for these techniques in the fields of the arts, medical systems, and research applications. We believe