Authors :
Nidhi Dewangan; Megha Singh; Vijayant Verma
Volume/Issue :
Volume 9 - 2024, Issue 4 - April
Google Scholar :
https://tinyurl.com/yype97z8
Scribd :
https://tinyurl.com/2nvt4624
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24APR1676
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
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.
Keywords :
Transformers, Music Generation, Compositions, Self-Attention Mechanism.
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.
Keywords :
Transformers, Music Generation, Compositions, Self-Attention Mechanism.
