Performance of the Dejavu audio fingerprinting framework in music identification in movies

Bachelor Thesis (2021)
Author(s)

N. Struharová (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Contributor(s)

Jeahun Kim – Mentor (TU Delft - Multimedia Computing)

C.C.S. Liem – Graduation committee member (TU Delft - Multimedia Computing)

Jesse Krijthe – Graduation committee member (TU Delft - Pattern Recognition and Bioinformatics)

Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright
© 2021 Natália Struharová
More Info
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Publication Year
2021
Language
English
Copyright
© 2021 Natália Struharová
Graduation Date
02-07-2021
Awarding Institution
Delft University of Technology
Project
CSE3000 Research Project
Programme
Computer Science and Engineering
Faculty
Electrical Engineering, Mathematics and Computer Science
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Abstract

Audio fingerprinting is one of the standard solutions for music identification. The underlying technique is designed to be robust to signal degradation such that music can be identified despite its presence. One of the newly emerged applications of a possibly challenging nature is music identification in movies. This paper examines the audio fingerprinting framework Dejavu by evaluating its performance against an existing benchmark created for the context of music identification in movies. The results show that Dejavu’s performance matches the expectations derived from the implementation and previous testing, and can be reconfigured to improve the performance in terms of the benchmark.

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