A METHOD FOR GENERATION OF MULTITRACK SYMBOLIC COMPOSITIONS USING GENERATIVE ARTIFICIAL INTELLIGENCE

Abstract

This study explores a method for symbolic music generation using artificial intelligence techniques. Unlike traditional rule-based or statistical approaches, the proposed method employs deep generative models to automatically create multi-track, polyphonic musical compositions.  
As part of the research, a comparative experiment was conducted to assess the quality of music generation by different architectural variants of the models. The main task of this stage was to determine the ability of the models to reproduce key characteristics of musical logic. 
The focus is placed on capturing the temporal structure of music and ensuring harmonic and rhythmic coherence across multiple instrument tracks. A model architecture is introduced that generates music in the form of piano-roll representations, treating musical bars as fundamental units. The system supports both autonomous generation and human-AI collaboration, where the model can accompany a user-provided melody with additional tracks. A specially prepared dataset of symbolic music was used for training, and a series of objective metrics were developed to evaluate the quality of the generated output.  

The proposed architecture ensures the formation of coherent musical phrases while preserving rhythm, harmony, and inter-track interaction. Analysis of intermediate results showed a gradual increase in the quality of generation: from chaotic sounds to ordered structures, which indicates the effectiveness of training. Results from both quantitative analysis and a subjective user study demonstrate that the method is capable of producing musically coherent and structurally consistent compositions. This research contributes to the development of AI-assisted tools for creative music production and expands the possibilities of automated composition in the symbolic domain.