A Combination of Multi-Objective Genetic Algorithm and Deep Learning for Music Harmony Generation
Maryam Majidi, Rahil Mahdian Toroghi

TL;DR
This paper introduces a multi-objective genetic algorithm combined with deep learning to generate polyphonic music that adheres to music theory and satisfies listener preferences, addressing the challenge of evaluating music quality.
Contribution
It proposes a novel approach integrating multi-objective genetic algorithms with Bi-LSTM models to generate harmonically correct and listener-pleasing music.
Findings
Generated music follows music theory and grammar.
Music pieces are rated positively by experts and listeners.
The method produces stylistically consistent and harmonic compositions.
Abstract
Automatic Music Generation (AMG) has become an interesting research topic for many scientists in artificial intelligence, who are also interested in the music industry. One of the main challenges in AMG is that there is no clear objective evaluation criterion that can measure the music grammar, structural rules, and audience satisfaction. Also, original music contains different elements that should work together, such as melody, harmony, and rhythm; but in the most of previous works, AMG works only for one element (e.g., melody). Therefore, in this paper, we propose a Multi-Objective Genetic Algorithm (MO-GA) to generate polyphonic music pieces, considering grammar and listener satisfaction. In this method, we use three objective functions. The first objective function is the accuracy of the generated music piece, based on music theory; and the other two objective functions are modeled…
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Taxonomy
TopicsMusic Technology and Sound Studies · Music and Audio Processing
