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Acapella-based music generation with sequential models utilizing discrete cosine transform
IAES International Journal of Artificial Intelligence
ISSN 2252-8938, 2089-4872
Terbit 2024-09-01
Penerbit Institute of Advanced Engineering and Science
Bahasa eng
Abstrak
Making musical instruments that accompany vocals in a song depends on the mood quality and the music composer’s creativity. The model created by other researchers has restrictions that include being limited to musical instrument digital interface files and relying on recurrent neural networks (RNN) or Transformers for the recursive generation of musical notes. This research offers the world’s first model capable of automatically generating musical instruments accompanying human vocal sounds. The model we created is divided into three types of sound input: short input, combed input, and frequency sound based on the discrete cosine transform (DCT). By combining the sequential models such as Autoencoder and gated recurrent unit (GRU) models, we will evaluate the performance of the resulting model in terms of loss and creativity. The best model has a performance evaluation that resulted in an average loss of 0.02993620155. The hearing test results from the sound output produced in the frequency range 0-1,600 Hertz can be heard clearly, and the tones are quite harmonious. The model has the potential to be further developed in future research in the field of sound processing.
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Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc)
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<dc:title xml:lang="en-US">Acapella-based music generation with sequential models utilizing discrete cosine transform</dc:title>
<dc:creator>Saputra, Julian</dc:creator>
<dc:creator>Prasetiadi, Agi</dc:creator>
<dc:creator>Kresna, Iqsyahiro</dc:creator>
<dc:subject xml:lang="en-US">Artificial Intelligence; Deep Learning; Music Technology; Sound Processing</dc:subject>
<dc:subject xml:lang="en-US">Autoencoder; Discrete cosine transform; Gated recurrent unit; Music instrument; Recurrent neural network</dc:subject>
<dc:description xml:lang="en-US">Making musical instruments that accompany vocals in a song depends on the mood quality and the music composer’s creativity. The model created by other researchers has restrictions that include being limited to musical instrument digital interface files and relying on recurrent neural networks (RNN) or Transformers for the recursive generation of musical notes. This research offers the world’s first model capable of automatically generating musical instruments accompanying human vocal sounds. The model we created is divided into three types of sound input: short input, combed input, and frequency sound based on the discrete cosine transform (DCT). By combining the sequential models such as Autoencoder and gated recurrent unit (GRU) models, we will evaluate the performance of the resulting model in terms of loss and creativity. The best model has a performance evaluation that resulted in an average loss of 0.02993620155. The hearing test results from the sound output produced in the frequency range 0-1,600 Hertz can be heard clearly, and the tones are quite harmonious. The model has the potential to be further developed in future research in the field of sound processing.</dc:description>
<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
<dc:contributor xml:lang="en-US">Department of Informatics</dc:contributor>
<dc:contributor xml:lang="en-US">Institut Teknologi Telkom Purwokerto</dc:contributor>
<dc:date>2024-09-01</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-US"></dc:type>
<dc:format>application/pdf</dc:format>
<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/23656</dc:identifier>
<dc:identifier>10.11591/ijai.v13.i3.pp3371-3380</dc:identifier>
<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 13, No 3: September 2024; 3371-3380</dc:source>
<dc:source>2252-8938</dc:source>
<dc:source>2089-4872</dc:source>
<dc:source>10.11591/ijai.v13.i3</dc:source>
<dc:language>eng</dc:language>
<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/23656/14146</dc:relation>
<dc:rights xml:lang="en-US">Copyright (c) 2024 Institute of Advanced Engineering and Science</dc:rights>
<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>
Sitasi dan pengindeksan
Jumlah menurut tiap indeks pada 13 Nov 2026; setiap indeks menghitung sitasi dengan cara berbeda.
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Sitasi artikel ini
APA 7
Saputra, J., Prasetiadi, A., & Kresna, I. (2024). Acapella-based music generation with sequential models utilizing discrete cosine transform. IAES International Journal of Artificial Intelligence, 13(3), 3371–3380. https://doi.org/10.11591/ijai.v13.i3.pp3371-3380
Salin
IEEE
J. Saputra, A. Prasetiadi, and I. Kresna, “Acapella-based music generation with sequential models utilizing discrete cosine transform,” IAES International Journal of Artificial Intelligence, vol. 13, no. 3, pp. 3371–3380, 2024, doi: 10.11591/ijai.v13.i3.pp3371-3380.
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