Skripsi

KLASIFIKASI GENRE MUSIK MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK PADA DATASET MEL-SPECTROGRAM. 

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Penilaian

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As music genres diversify and online music libraries grow, the need for automated, accurate genre classification has become essential for efficient music organization and recommendation. In this research, we developed a music genre classifier using a custom Convolutional Neural Network (CNN) trained on mel-spectrogram images derived from the GTZAN dataset. The GTZAN dataset is a widely used benchmark in music genre classification and comprises 1,000 music audio samples, each 30 seconds in duration, categorized into 10 distinct genres: blues, classical, country, disco, hiphop, jazz, metal, pop, reggae, and rock. These audio samples were preprocessed by converting them to mel-spectrograms using a base frequency of 22,050 Hz, capturing the spectral characteristics essential for genre differentiation. The dataset was then split into an 80:20 ratio for training and validation. After exploring and testing 20 CNN architectures, 10 models achieved over 50% validation accuracy, with the best model achieves 69.5% accuracy. This work highlights the potential and challenges of designing an effective CNN model specifically for genre classification tasks.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2407006905	T161995	T1619952024	Central Library (REFERENCE)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1619952024

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2024

Collation

xiv, 165 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

006.320 7

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

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Subject(s)

Prodi Teknik Informatika
Neural networks; Music genre classification

Specific Detail Info

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Statement of Responsibility

TUTI

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