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Parsing Musical Structure to Enable Meaningful Variations

Authors: Maziar Kanani; Seán O'Leary; James McDermott;

Parsing Musical Structure to Enable Meaningful Variations

Abstract

https://aimc2023.pubpub.org/pub/xp8k765e This paper presents a novel rule-based approach for generating music by varying existing tunes. We parse each tune to find the Pathway Assembly (PA) [1], that is a structure representing all repetitions in the tune. The Sequitur algorithm [2] is used for this. The result is a grammar. We then carry out mutation on the grammar, rather than on a tune directly. There are potentially 19 types of mutations such as adding, removing, swapping or reversing parts of the grammar that can be applied to the grammars. The system employs one of the mutations randomly in this step to automatically manipulate the grammar. Following the mutation, we need to expand the grammar which returns a new tune. The output after 1 or more mutations will be a new tune related to the original tune. Our study examines how tunes change gradually over the course of multiple mutations. Edit distances, structural complexity and length of the tunes are used to show how a tune is changed after multiple mutations. In addition, the size of effect of each mutation type is analyzed. As a final point, we review the musical aspect of the output tunes. It should be noted that the study only focused on generating new pitch sequences. The study is based on an Irish traditional tune dataset and a list of integers has been used to represent each tune’s pitch values.

Keywords

FOS: Computer and information sciences, Sound (cs.SD), Sound, Artificial Intelligence (cs.AI), Artificial Intelligence, Audio and Speech Processing (eess.AS), FOS: Electrical engineering, electronic engineering, information engineering, Neural and Evolutionary Computing, Neural and Evolutionary Computing (cs.NE), Audio and Speech Processing

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