<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This study explores the integration of music and technology, illustrating their potential to collaboratively push the boundaries of musical exploration. Despite traditionally being viewed as unrelated, the combination of these two fields can significantly contribute to the progress of musical development. This study uses advanced computational methods to build a dataset filled with symbolic musical sequences that belong to a specific genre. This dataset is shown to be highly accurate and provides a detailed analysis of frequencies when examined closely, highlighting its quality and depth. We subject our dataset to comparative analysis with the renowned MAESTRO dataset, employing chromagrams to examine audio signals, rhythms, chords, solos, and note patterns in MIDI format through a variety of methods. This comparison underscores the superior quality of our sequences relative to those in the MAESTRO dataset, emphasizing the meticulousness of our sequence creation process. Moreover, we conduct internal evaluations of our dataset using both three-dimensional and two-dimensional approaches to melody representation, confirming its viability for future scholarly work. This effort seeks to enhance the music field by integrating computer science insights and methodologies, expanding the scope for future music technology research. It highlights the collaborative potential between musical creativity and technological advances in ongoing studies.</p>
</abstract>

This study explores the integration of music and technology, illustrating their potential to collaboratively push the boundaries of musical exploration. Despite traditionally being viewed as unrelated, the combination of these two fields can significantly contribute to the progress of musical development. This study uses advanced computational methods to build a dataset filled with symbolic musical sequences that belong to a specific genre. This dataset is shown to be highly accurate and provides a detailed analysis of frequencies when examined closely, highlighting its quality and depth. We subject our dataset to comparative analysis with the renowned MAESTRO dataset, employing chromagrams to examine audio signals, rhythms, chords, solos, and note patterns in MIDI format through a variety of methods. This comparison underscores the superior quality of our sequences relative to those in the MAESTRO dataset, emphasizing the meticulousness of our sequence creation process. Moreover, we conduct internal evaluations of our dataset using both three-dimensional and two-dimensional approaches to melody representation, confirming its viability for future scholarly work. This effort seeks to enhance the music field by integrating computer science insights and methodologies, expanding the scope for future music technology research. It highlights the collaborative potential between musical creativity and technological advances in ongoing studies.

Comparative Symbolic Analysis of the Ethno-Fusion Genre: Insights and Perspectives

SEEU Review

This work is licensed under the Creative Commons Attribution 4.0 International License.

This study explores the integration of music and technology, illustrating their potential to collaboratively push the boundaries of musical exploration....

Comparative Symbolic Analysis of the Ethno-Fusion Genre: Insights and Perspectives

Publié en ligne: 26 juin 2024

Pages: 65 - 80

DOI: https://doi.org/10.2478/seeur-2024-0075

Mots clés
MIDI, Dataset, Analysis, Frequencies, Music, Melodic

© 2024 Valton Kamberaj et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Comparative Symbolic Analysis of the Ethno-Fusion Genre: Insights and Perspectives

Publié en ligne: 26 juin 2024

Pages: 65 - 80

DOI: https://doi.org/10.2478/seeur-2024-0075

Mots clésMIDI, Dataset, Analysis, Frequencies, Music, Melodic

© 2024 Valton Kamberaj et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
MIDI, Dataset, Analysis, Frequencies, Music, Melodic