![]() ![]() generating a four-part chorale based on a user-given melody, has long been closely associated with J.S. aesthetically plausible) as assessed by an expert - a harmony teacher. in line with music harmonization theory) and also “nice to listen to” (i.e. The proposed method generates solutions which are technically correct (i.e. ![]() ![]() In particular, we show how could generated harmonizations be modelled by means of adjusting the relevance of particular fitness function components. The way the fitness function is constructed and tuned towards better quality harmonizations is discussed in the context of music theory and technical EA implementation. This design allows for its flexible modification and extension. The fitness function is composed of several modules, with each module consisting of smaller parts. It is expected that harmonizations constructed in accordance to these rules would be formally correct in terms of music theory and, additionally, would follow less-formalised aesthetic requirements and expectations. Specifically, we propose an Evolutionary Algorithm (EA) capable of constructing melodic line harmonization with given harmonic functions, based on the rules of music composing which are applied in the fitness function. This paper examines potential applicability and efficacy of Artificial Intelligence (AI) methods in automatic music generation. ![]()
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