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Music Genre Classification Using Mel Frequency Cepstral Coefficients and Artificial Neural Networks: A Novel Approach

Scientific Journal of Informatics · Vol. 11 No. 4 (2024) · hlm. 937–948

DOI
10.15294/sji.v11i4.13660
ISSN
2407-7658, 2460-0040
Terbit
2024-12-16
Penerbit
Universitas Negeri Semarang
Bahasa
eng

Abstrak

Purpose: Music is an artistic expression with many categories in various genres and styles, characterized by its melodic and harmonic compositions. Music genre classification is crucial because genres serve as descriptors commonly used to organize large music collections, especially on the internet and in widely used applications like JOOX and Spotify. The aim of this research is to implement the Mel Frequency Cepstral Coefficients (MFCC) feature extraction method to generate numerical features from a set of specific music tracks. This collection of information will then be classified using machine learning. Methods: The method used in this study begins with combining the "GTZAN Dataset - Music Genre Classification" with additional data from TikTok and YouTube. The total dataset consists of 1,200 audio files, divided into 12 classes. The MFCC extraction process generates numerical representations of acoustic characteristics, which are then processed using Artificial Neural Networks. Result: The experiments demonstrate that increasing the amount of data is crucial, as it can enhance both variation and accuracy. The average accuracy achieved in this study is 91.42%, while the highest accuracy reaches 92.16%. These findings indicate that this study outperforms previous studies. Novelty: The novelty of this research lies in the integration of dynamic social media data (TikTok and YouTube) to enrich the standard GTZAN dataset, the repetition of the MFCC feature extraction process, and the combination of MFCC with Artificial Neural Networks (ANN).

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  • Institut Teknologi Kalimantan
  • State University of Semarang
  • National Research and Innovation Agency
  • Newcastle University
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
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Rekan penulisOrganisasiArtikel bersama
Alamsyah AlamsyahInstitut Teknologi Kalimantan7
Much Aziz MuslimState University of Semarang3
Endang SugihartiState University of Semarang3
Amalia Ika WulandariAfiliasi tidak diketahui3
Riza ArifudinState University of Semarang2
Chris Jeremy Verian SitorusInstitut Teknologi Kalimantan2
Muhammad Uswah PawaraInstitut Teknologi Kalimantan2
Rodlian Jamal IkhwaniInstitut Teknologi Kalimantan2
Suardi SuardiAfiliasi tidak diketahui2
Amalia Ika WulandariInstitut Teknologi Kalimantan2
Suardi SuardiInstitut Teknologi Kalimantan2
Lisa AriyantiState University of Semarang2
Budi PrasetiyoState University of Semarang1
Jumanto JumantoState University of Semarang1
Rofik RofikState University of Semarang1
Charles Sambera KalaAfiliasi tidak diketahui1
Niti Gede KumandangInstitut Teknologi Kalimantan1
Arif Nur IskandarAfiliasi tidak diketahui1
Taufik HidayatNational Research and Innovation Agency1
Taufik HidayatInstitut Teknologi Kalimantan1
Arif Nur IskandarInstitut Teknologi Kalimantan1
Abrari Noor HasmiAfiliasi tidak diketahui1
Abrari Noor HasmiInstitut Teknologi Kalimantan1
Asram Dio PratamaInstitut Teknologi Kalimantan1
Rodlian Jamal IkhwaniAfiliasi tidak diketahui1
Irvan SetiawanInstitut Teknologi Kalimantan1
Destyariani Liana PutriAfiliasi tidak diketahui1
Alan Dicky Swans SaragihAfiliasi tidak diketahui1
Anggoronadhi DianiswaraAfiliasi tidak diketahui1
Destyariani Liana PutriInstitut Teknologi Kalimantan1
Alan Dicky Swans SaragihInstitut Teknologi Kalimantan1
Anggoronadhi DianiswaraInstitut Teknologi Kalimantan1
Wira SetiawanAfiliasi tidak diketahui1
Muhammad Uswah PawaraAfiliasi tidak diketahui1
Andi Mursid Nugraha ArifuddinAfiliasi tidak diketahui1
Samsu Dlukha NurcholiqAfiliasi tidak diketahui1
Wira SetiawanNewcastle University1

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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/journals/sji/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en">Music Genre Classification Using Mel Frequency Cepstral Coefficients and Artificial Neural Networks: A Novel Approach</dc:title>
	<dc:creator xml:lang="en">Alamsyah, Alamsyah</dc:creator>
	<dc:creator xml:lang="en">Ardiansyah, Fahmi</dc:creator>
	<dc:creator xml:lang="en">Kholiq, Abdul</dc:creator>
	<dc:subject xml:lang="en">Music genre classification</dc:subject>
	<dc:subject xml:lang="en">Mel frequency cepstral coefficients</dc:subject>
	<dc:subject xml:lang="en">Artificial neural networks</dc:subject>
	<dc:description xml:lang="en">Purpose: Music is an artistic expression with many categories in various genres and styles, characterized by its melodic and harmonic compositions. Music genre classification is crucial because genres serve as descriptors commonly used to organize large music collections, especially on the internet and in widely used applications like JOOX and Spotify. The aim of this research is to implement the Mel Frequency Cepstral Coefficients (MFCC) feature extraction method to generate numerical features from a set of specific music tracks. This collection of information will then be classified using machine learning.
Methods: The method used in this study begins with combining the "GTZAN Dataset - Music Genre Classification" with additional data from TikTok and YouTube. The total dataset consists of 1,200 audio files, divided into 12 classes. The MFCC extraction process generates numerical representations of acoustic characteristics, which are then processed using Artificial Neural Networks.
Result: The experiments demonstrate that increasing the amount of data is crucial, as it can enhance both variation and accuracy. The average accuracy achieved in this study is 91.42%, while the highest accuracy reaches 92.16%. These findings indicate that this study outperforms previous studies.
Novelty: The novelty of this research lies in the integration of dynamic social media data (TikTok and YouTube) to enrich the standard GTZAN dataset, the repetition of the MFCC feature extraction process, and the combination of MFCC with Artificial Neural Networks (ANN).</dc:description>
	<dc:publisher xml:lang="en">Universitas Negeri Semarang</dc:publisher>
	<dc:date>2024-12-16</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/journals/sji/article/view/13660</dc:identifier>
	<dc:identifier>10.15294/sji.v11i4.13660</dc:identifier>
	<dc:source xml:lang="en">Scientific Journal of Informatics; Vol. 11 No. 4: Special Issue; 937-948</dc:source>
	<dc:source>2407-7658</dc:source>
	<dc:source>2460-0040</dc:source>
	<dc:source>10.15294/sji.v11i4</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/journals/sji/article/view/13660/2221</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2024 Scientific Journal of Informatics</dc:rights>
</oai_dc:dc>