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Published Paper Details:
Paper Title
Music Genre Classification
Authors
Bharathi Erothi, K.T.Krishna Kumar
Keywords
Music Genre Classification, Machine Learning, Audio Processing, Feature Extraction, Convolutional Neural Networks (CNN), Genre Detection, Audio Signal, Music Dataset (e.g., GTZAN, FMA), Audio File Formats (e.g., MP3, WAV), Signal Processing.
Abstract
Music genre is a classification system that classifies music into different types. The classification of music
genre is very important to make a selection of songs from a large collection of data. Different features have been
extracted as it is essential for the genre classification. We use convolution neural networks to classify music genres.
The project aims to create an automated system for classification models of music genres. We use the most publicly
used data set which is GTZAN for evaluation of music genres. It contains 10 genres (blues, classical, country, disco,
hip-hop, jazz, metal, pop, reggae, rock). The audio file is sent as input and the classification is done. CNN is used to
classify the data furthermore.
IJCRT's Publication Details
Unique Identification Number - IJCRT2407601
Paper ID - 266104
Page Number(s) - f260-f269
Pubished in - Volume 12 | Issue 7 | July 2024
DOI (Digital Object Identifier) -
Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882
E-ISSN Number - 2320-2882
Cite this article
Bharathi Erothi, K.T.Krishna Kumar, "Music Genre Classification", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.12, Issue 7, pp.f260-f269, July 2024, Available at :http://www.ijcrt.org/papers/IJCRT2407601.pdf
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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