Authors :
Nandini Vatsya; Aaryan Thipse; Priyansh Dixit; Rajnandini Dafe; Kunal Shejul
Volume/Issue :
Volume 9 - 2024, Issue 4 - April
Google Scholar :
https://tinyurl.com/2s45vek5
Scribd :
https://tinyurl.com/rsx2fcjw
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24APR2001
Abstract :
This project aims to develop a novel
approach for piano melody generation using Recurrent
Neural Networks (RNN) and Long Short-Term Memory
(LSTM) models in deep learning.The suggested models
will be trained on a dataset of MIDI files with piano
melodies to use sequential learning capabilities and
capture the complex patterns and relationships present
in musical compositions. [1] The project aims to gen-
erate a variety of melodies that are both musically
coherent and diverse by experimenting with various
network designs, hyperparameters, and training
procedures. The developed tunes will be evaluated
primarily on their originality, conformity to stylistic
elements, and general quality. The results of this study
could lead to new developments in AI-driven music
composition as well as opportunities for computational
creativity in the music industry.
Keywords :
Measurement; Recurrent Neural Networks; Instruments; Music; Reinforcement Learning; Signal Processing; Generative Adversarial Networks; Music Generation; Melody; GAN; LSTM.
This project aims to develop a novel
approach for piano melody generation using Recurrent
Neural Networks (RNN) and Long Short-Term Memory
(LSTM) models in deep learning.The suggested models
will be trained on a dataset of MIDI files with piano
melodies to use sequential learning capabilities and
capture the complex patterns and relationships present
in musical compositions. [1] The project aims to gen-
erate a variety of melodies that are both musically
coherent and diverse by experimenting with various
network designs, hyperparameters, and training
procedures. The developed tunes will be evaluated
primarily on their originality, conformity to stylistic
elements, and general quality. The results of this study
could lead to new developments in AI-driven music
composition as well as opportunities for computational
creativity in the music industry.
Keywords :
Measurement; Recurrent Neural Networks; Instruments; Music; Reinforcement Learning; Signal Processing; Generative Adversarial Networks; Music Generation; Melody; GAN; LSTM.
