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Comparison of Deep Learning and Machine Learning in Music Genre Categorization

Joseph Fernandez, Saviour Nickolas Derel (2023) Comparison of Deep Learning and Machine Learning in Music Genre Categorization. Masters thesis, Dublin, National College of Ireland.

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Humans invented the concept of musical genres to categorize and describe different types of music. The extensive music libraries that are accessible on the internet are frequently organized using genre hierarchies. Music information retrieval systems would benefit greatly from the addition of automatic musical genre categorization, which may supplement or even take the place of the human user in this process where the genres are categorized manually sometimes. There have been variable degrees of success with different music collections, data formats, learning algorithms, and types of neural networks applied. Music indexing and information retrieval both benefit greatly from automatic musical genre classification. The automated classification of musical genres using a combination of machine learning and deep learning algorithms is provided in this study as an effective and efficient approach. The mp3-formatted audio tracks from the FMA dataset are processed for feature extraction using the Mel-spectrogram method for feature retrieval using the ‘librosa’ library. Then the array of converted data from the Mel-spectrogram is used by various classification models. Eight machine-learning includes Naıve
Bayes, KNN, Random Forest, XG-Boost classifier, SVM, etc, are used along with the CNN and CNN-LSTM neural network models. On final evaluation, the CNN neural classifier performed better in terms of train model accuracy 0.92 and f1 score than any other classification models and this includes a few parameters tuned for better classification results.

Item Type: Thesis (Masters)
Milosavljevic, Vladimir
Subjects: M Music and Books on Music > M Music
Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Tamara Malone
Date Deposited: 18 May 2023 16:53
Last Modified: 18 May 2023 16:53

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