Melola Discovery

← Melola Discovery

Open access Jurnal SINTA 1

Music fingerprinting based on bhattacharya distance for song and cover song recognition

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 9 No. 2 (2019)

DOI
10.11591/ijece.v9i2.pp1036-1044
ISSN
2088-8708, 2088-8708
Published
2019-04-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-nc/4.0

Abstract

People often have trouble recognizing a song especially, if the song is sung by a not original artist which is called cover song. Hence, an identification system might be used to help recognize a song or to detect copyright violation. In this study, we try to recognize a song and a cover song by using the fingerprint of the song represented by features extracted from MPEG-7. The fingerprint of the song is represented by Audio Signature Type. Moreover, the fingerprint of the cover song is represented by Audio Spectrum Flatness and Audio Spectrum Projection. Furthermore, we propose a sliding algorithm and k-Nearest Neighbor (k-NN) with Bhattacharyya distance for song recognition and cover song recognition. The results of this experiment show that the proposed fingerprint technique has an accuracy of 100% for song recognition and an accuracy of 85.3% for cover song recognition.

Collaboration network of the authors

32 authors · 68 co-authorship links · 24 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Institut Teknologi Sepuluh Nopember
  • Telkom University
  • Universitas Dian Nuswantoro
  • Politeknik Negeri Malang
  • University of Brawijaya
  • Universiti Malaysia Terengganu
  • Mercu Buana University
  • Other organisations
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Kelly Rossa SungkonoInstitut Teknologi Sepuluh Nopember4
Chastine FatichahInstitut Teknologi Sepuluh Nopember2
Imam GhozaliInstitut Teknologi Sepuluh Nopember2
Rachmad AbdullahInstitut Teknologi Sepuluh Nopember2
Tohari AhmadInstitut Teknologi Sepuluh Nopember1
Solichul HudaUniversitas Dian Nuswantoro1
Faisal RahutomoPoliteknik Negeri Malang1
Bilqis AmaliahInstitut Teknologi Sepuluh Nopember1
Margo PudjiantaraInstitut Teknologi Sepuluh Nopember1
Bayu PriyambadhaUniversity of Brawijaya1
Nurul Fajrin AriyaniInstitut Teknologi Sepuluh Nopember1
Sarwosri SarwosriInstitut Teknologi Sepuluh Nopember1
Alief Yoga PriyantoInstitut Teknologi Sepuluh Nopember1
I Gusti Agung Chintya Prema DewiInstitut Teknologi Sepuluh Nopember1
Muhammad Suzuri HitamUniversiti Malaysia Terengganu1
Fika Hastarita RachmanInstitut Teknologi Sepuluh Nopember1
Siti RochimahInstitut Teknologi Sepuluh Nopember1
Bambang JokonowoMercu Buana University1
Jan ClaesGhent University1
Agus Zainal ArifinInstitut Teknologi Sepuluh Nopember1
Anto Satriyo NugrohoBadan Pengkajian dan Penerapan Teknologi1
Umi SalamahSebelas Maret University1
Ismail Eko Prayitno RoziAffiliation unknown1
Puji Budi Setia AsihEijkman Institute for Molecular Biology1
Agus Tri HaryonoInstitut Teknologi Sepuluh Nopember1
Salsabila SalsabilaInstitut Teknologi Sepuluh Nopember1
Ahmad SaikhuInstitut Teknologi Sepuluh Nopember1
Hermawan Bin FauzanInstitut Teknologi Sepuluh Nopember1
Suhariyanto SuhariyantoInstitut Teknologi Sepuluh Nopember1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Music fingerprinting based on bhattacharya distance for song and cover song recognition</dc:title><dc:creator>Sarno, Riyanarto</dc:creator><dc:creator>Wijaya, Dedy Rahman</dc:creator><dc:creator>Mahardika, Muhammad Nezar</dc:creator><dc:description>&lt;jats:p&gt;People often have trouble recognizing a song especially, if the song is sung by a not original artist which is called cover song. Hence, an identification system might be used to help recognize a song or to detect copyright violation. In this study, we try to recognize a song and a cover song by using the fingerprint of the song represented by features extracted from MPEG-7. The fingerprint of the song is represented by Audio Signature Type. Moreover, the fingerprint of the cover song is represented by Audio Spectrum Flatness and Audio Spectrum Projection. Furthermore, we propose a sliding algorithm and k-Nearest Neighbor (k-NN) with Bhattacharyya distance for song recognition and cover song recognition. The results of this experiment show that the proposed fingerprint technique has an accuracy of 100% for song recognition and an accuracy of 85.3% for cover song recognition.&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2019-04-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v9i2.pp1036-1044</dc:identifier><dc:identifier>10.11591/ijece.v9i2.pp1036-1044</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 9 No 2 (2019); 1036</dc:source><dc:source>2088-8708</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://ijece.iaescore.com/index.php/IJECE/article/viewFile/14592/11607</dc:relation><dc:rights>http://creativecommons.org/licenses/by-nc/4.0</dc:rights></oai_dc:dc>