Automatic music generation has become an epicenter research topic for many scientists in artificial intelligence, who are also interested in the music industry. Being a balanced combination of math and art, music in collaboration with A.I. can simplify the generation process for new musical pieces, and ease the interpretation of it to a tangible level. On the other hand, the artistic nature of music and its mingling with the senses and feelings of the composer makes the artificial generation and mathematical modeling of it infeasible. In fact, there are no clear evaluation measures that can combine the objective music grammarand structure with the subjective audience satisfactiongoal . Also, original music contains different elements that it is inevitable to put together. Therefore, in this paper, a method based on a genetic multi-objective evolutionary optimization algorithm for the generation of polyphonic music (melody with rhythm and harmony or appropriate chords) is introduced in which three specific goals determine the qualifications of the music generated. One of the goals is the rules and regulations of music, which, along with the other two goals, including the scores of music experts and ordinary listeners , fits the cycle of evolution to get the most optimal response . The scoring of experts and listeners separately is modeled using a Bi-LSTM neural network and has been incorporated in the fitness function of the algorithm . The results show that the proposed method is able to generate difficult and pleasant pieces with desired styles and lengths, along with harmonic sounds that follow the grammar while attracting the listener, at the same time .
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 .
R
music generation
Music Generation (AMG)
R
epicenter
interesting
R
Being a balanced combination of math and art, music in collaboration with A.I. can simplify the generation process for new musical pieces, and ease the interpretation of it to a tangible level. On the other hand, the artistic nature of music and its mingling with the senses and feelings of the composer makes the artificial generation and mathematical modeling of it infeasible. In fact, there are no clear evaluation measures that can combine the objective music grammarand structure with the subjective audience satisfactiongoal
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
R
it is inevitable to put together.
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).
R
a method based on a genetic multi-objective evolutionary optimization algorithm for the generation of polyphonic music (melody with rhythm and harmony or appropriate chords) is introduced in which three specific goals determine the qualifications of the music generated. One of the goals is the rules and regulations of music, which, along with
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
R
goals, including the scores of
objective functions are modeled scores provided by
D
, fits the cycle of evolution to get the most optimal response
R
is modeled using a
are modeled using Bi-directional Long Short-Term Memory (
R
neural network and has been incorporated in the fitness function of the algorithm
) neural networks. The proposed music generation system tries to maximize mentioned objective functions to generate a new piece of music, including melody and harmony
R
is able to generate difficult and
can generate
D
while attracting the listener, at the same time