Music Genre Recommendations Based on Spectrogram Analysis Using Convolutional Neural Network Algorithm with RESNET-50 and VGG-16 Architecture

nyoman purnama

Abstract


Recommendations are a very useful tool in many industries. Recommendations provide the best selection of what the user wants and provide satisfaction compared to ordinary searches. In the music industry, recommendations are used to provide songs that have similarities in terms of genre or theme. There are various kinds of genres in the world of music, including pop, classic, reggae and others. With genre, the difference between one song and another can be heard clearly. This genre can be analyzed by spectrogram analysis. In this study, a spectrogram analysis was developed which will be the input feature for the Convolutional Neural Network. CNN will classify and provide song recommendations according to what the user wants. In addition, testing was carried out with two different architectures from CCN, namely VGG-16 and RESNET-50. From the results of the study obtained, the best accuracy results were obtained by the VGG-16 model with 20 epochs with accuracy 60%, compared to the RESNET-50 model with more than 20 epochs. The results of the recommendations generated on the test data obtained a good similarity value for VGG-16 compared to RESNET-50.


Keywords


Recommendation, VGG16, Resnet50, CNN, Spectogram, Music

Full Text:

PDF

References


Y. M. G. Costa, L. S. Oliveira, A. L. Koericb, and F. Gouyon, “Music genre recognition using spectrograms,” Int. Conf. Syst. Signals, Image Process., pp. 151–154, 2011.

A. George, S. Suneesh, S. Sreelakshmi, and T. E. Paul, “Music Recommendation System Using CNN,” vol. 9, no. 6, pp. 4197–4200, 2020.

C. R. Wairata, E. R. Swedia, and M. Cahyanti, “Pengklasifikasian Genre Musik Indonesia Menggunakan Convolutional Neural Network,” Sebatik, vol. 25, no. 1, pp. 255–261, 2021, doi: 10.46984/sebatik.v25i1.1286.

K.-C. Hsu, S.-Y. Chou, Y.-H. Yang, and T.-S. Chi, “Neural Network Based Next-Song Recommendation,” 2016, [Online]. Available: http://arxiv.org/abs/1606.07722.

Faiz Nashrullah, Suryo Adhi Wibowo, and Gelar Budiman, “The Investigation of Epoch Parameters in ResNet-50 Architecture for Pornographic Classification,” J. Comput. Electron. Telecommun., vol. 1, no. 1, pp. 1–8, 2020, doi: 10.52435/complete.v1i1.51.

M. Talo, O. Yildirim, U. B. Baloglu, G. Aydin, and U. R. Acharya, “Convolutional neural networks for multi-class brain disease detection using MRI images,” Comput. Med. Imaging Graph., vol. 78, p. 101673, Dec. 2019, doi: 10.1016/J.COMPMEDIMAG.2019.101673.

D. Lionel, R. Adipranata, and E. Setyati, “Klasifikasi Genre Musik Menggunakan Metode Deep Learning Convolutional Neural Network dan Mel- Spektrogram,” J. Infra Petra, vol. 7, no. 1, pp. 51–55, 2019, [Online]. Available: http://publication.petra.ac.id/index.php/teknik-informatika/article/view/8044.

Adiyansjah, A. A. S. Gunawan, and D. Suhartono, “Music recommender system based on genre using convolutional recurrent neural networks,” Procedia Comput. Sci., vol. 157, pp. 99–109, 2019, doi: 10.1016/j.procs.2019.08.146.

M. H. Ashshiddieqy, Jondri, and A. Rizal, “Klasifikasi Suara Paru Dengan Convolutional Neural Network (CNN),” eProceedings Eng., vol. 07, no. 02, pp. 8506–8512, 2020.

W. Setiawan, “Perbandingan Arsitektur Convolutional Neural Network Untuk Klasifikasi Fundus,” J. Simantec, vol. 7, no. 2, pp. 48–53, 2020, doi: 10.21107/simantec.v7i2.6551.




DOI: https://doi.org/10.31326/jisa.v5i1.1270

Refbacks

  • There are currently no refbacks.


Copyright (c) 2022 nyoman purnama

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.


JOURNAL IDENTITY

Journal Name: JISA (Jurnal Informatika dan Sains)
e-ISSN: 2614-8404, p-ISSN: 2776-3234
Publisher: Program Studi Teknik Informatika Universitas Trilogi
Publication Schedule: June and December 
Language: Indonesia & English
APC: The Journal Charges Fees for Publishing 
IndexingEBSCODOAJGoogle ScholarArsip Relawan Jurnal IndonesiaDirectory of Research Journals Indexing, Index Copernicus International, PKP IndexScience and Technology Index (SINTA, S4) , Garuda Index
OAI addresshttp://trilogi.ac.id/journal/ks/index.php/JISA/oai
Contactjisa@trilogi.ac.id
Sponsored by: DOI – Digital Object Identifier Crossref, Universitas Trilogi, Yayasan Damandiri

In Collaboration With: Indonesian Artificial Intelligent Ecosystem(IAIE), Relawan Jurnal IndonesiaJurnal Teknologi dan Sistem Komputer (JTSiskom)

 

 


JISA (Jurnal Informatika dan Sains) is Published by Program Studi Teknik Informatika, Universitas Trilogi under Creative Commons Attribution-ShareAlike 4.0 International License.