Authors :
Y. Swathi; N. Snigdha; I.Akhila; M. Sowmya; M. Balaji
Volume/Issue :
Volume 9 - 2024, Issue 4 - April
Google Scholar :
https://tinyurl.com/53ct5yp8
Scribd :
https://tinyurl.com/5xbfcs4n
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24APR2162
Abstract :
The Music Genre Classification model
automatically divides music into different genres using a
small number of audio files and a range of musical
attributes. This topic is highly relevant to the field of
music information retrieval since it provides a way to
organize and analyze large amounts of music files. For
MGC, standard machine learning techniques such as
SVM, KNN, Decision trees, and neural networks can be
applied. These algorithms are trained to recognize
different musical qualities and traits, which allows them
to categorize the audio files into different genres.
Numerous applications show that deep learning
algorithms—such as CNN, ANN, and others—perform
better than conventional machine learning algorithms.
Consequently, the CNN method is adjusted to perform
the categorization of music files. This classifies musical
genres using deep learning methods from CNN. To
evaluate the effectiveness of the MGC algorithms,
accuracy is used. Moreover, the impact of different
algorithms on MGC performance can be compared and
studied. It can be applied to automated music
recommendation systems, music production, and music
education.
Keywords :
Music Genre Classification, Deep Learning, Convolution Neural Network, Transfer Learning, Artificial Neural Network.
The Music Genre Classification model
automatically divides music into different genres using a
small number of audio files and a range of musical
attributes. This topic is highly relevant to the field of
music information retrieval since it provides a way to
organize and analyze large amounts of music files. For
MGC, standard machine learning techniques such as
SVM, KNN, Decision trees, and neural networks can be
applied. These algorithms are trained to recognize
different musical qualities and traits, which allows them
to categorize the audio files into different genres.
Numerous applications show that deep learning
algorithms—such as CNN, ANN, and others—perform
better than conventional machine learning algorithms.
Consequently, the CNN method is adjusted to perform
the categorization of music files. This classifies musical
genres using deep learning methods from CNN. To
evaluate the effectiveness of the MGC algorithms,
accuracy is used. Moreover, the impact of different
algorithms on MGC performance can be compared and
studied. It can be applied to automated music
recommendation systems, music production, and music
education.
Keywords :
Music Genre Classification, Deep Learning, Convolution Neural Network, Transfer Learning, Artificial Neural Network.
