A Combination of Multi-Objective Genetic Algorithm and Deep Learning for
  Music Harmony Generation
  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 scores provided by
music experts and ordinary listeners. The scoring of experts and listeners
separately are modeled using Bi-directional Long Short-Term Memory (Bi-LSTM)
neural networks. The proposed music generation system tries to maximize
mentioned objective functions to generate a new piece of music, including
melody and harmony. The results show that the proposed method can generate
pleasant pieces with desired styles and lengths, along with harmonic sounds
that follow the grammar.
