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Publication: IIP Proceedings Year: 2022, Month: November Page No: 19-26, ISSN/ISBN: 978-93-95632-70-6, DOI/Link: https://rsquarel.org/assets/docupload/rsl20235239B7C167ADC1E.pdf
MUSIC GENRE CLASSIFICATION
Area/Stream: Artificial Intelligence, Authors: Anantha Krishna G.K, Suhas A Bhyratae, Advithiya A Bangera, Maruthi, Deepraj Majalikar Keywords: K-Nearest Neighbor (k-NN); Support Vector Machine (SVM); music; genre; classification; features; Mel Frequency Cepstral Coefficients (MFCC); GTZAN Dataset; Convolution Neural Network (CNN); Recommendation System; Book Name /series: Futuristic Trends in Artificial Intelligence,Volume 2, Book 16, Chapter 2Publication: IIP Proceedings Year: 2022, Month: November Page No: 19-26, ISSN/ISBN: 978-93-95632-70-6, DOI/Link: https://rsquarel.org/assets/docupload/rsl20235239B7C167ADC1E.pdf
Abstract:
With the growth of online music databases and easy access to music content, people find it increasing hard to manage the songs that they listen to. Music genre classification is a vital activity that involves categorizing music genres from audio data. In the field of music information retrieval, music genre classification is frequently utilized. The proposed framework deals with three main steps: data pre-processing, feature extraction, and classification. Convolution Neural Network (CNN) is the method used to tackle music genre classification. The proposed system uses feature values of spectrograms generated from slices of songs as the input into a CNN to classify the songs into their music genres. A recommendation system is also implemented after the classification process. The recommendation system aims to recommend songs on each user’s preferences and interests. Extensive experiments carried out on the GTZAN dataset show the effectiveness of the proposed system with respect to other methods.
Cite this: Anantha Krishna G.K, Suhas A Bhyratae, Advithiya A Bangera, Maruthi, Deepraj Majalikar,"MUSIC GENRE CLASSIFICATION", Futuristic Trends in Artificial Intelligence,Volume 2, Book 16, Chapter 2, November, 2022, 19-26, 978-93-95632-70-6, https://rsquarel.org/assets/docupload/rsl20235239B7C167ADC1E.pdf
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