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Transformers have significantly revolutionized the music-creation process by their ability to generate intricate and captivating musical arrangements. By analyzing patterns and connections within music data, transformers can produce new compositions with remarkable accuracy and originality. This study explores the internal mechanisms of transformers in music generation and highlights their potential for advancing the field of musical composition. The ability of transformers to capture extensive relationships and contextual information makes them highly suitable for tasks related to music generation. Through self-attention mechanisms, transformers effectively model the dependencies between different time intervals in a musical sequence, resulting in the production of coherent and melodious compositions. This paper delves into the specific architectural elements of transformers that enable them to comprehend and generate musical sequences while also exploring potential applications for transformer-based systems in various creative contexts - emphasizing on significant impact they could have on evolving techniques used during music composition.
Dewangan et al. (Thu,) studied this question.