Analysing the performance of the OLAF framework in the context of identifying music in movies

Bachelor thesis (2021)

Authors

C.W.R. Hildebrand Electrical Engineering, Mathematics and Computer Science

Contributors

C.C.S. Liem (mentor)
Jaehun Kim (mentor)
J.H. Krijthe Pattern Recognition and Bioinformatics - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2021 Casper Hildebrand
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Published Date

02-07-2021

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

This paper presents the findings of a benchmark performed on the audio fingerprinting framework OLAF in the context of movie music. The goal is to find a music identification framework suitable for automatically identifying a song from a movie clip. This research aims to find how well OLAF performs in this context with regard to the criteria determined in the benchmark and to see if the performance can be increased. The OLAF framework makes use of parameters, which are individually tweaked and benchmarked with synthesised data. These findings are then combined to find a set of parameters that is used to perform this same benchmark on real clips from movies. The found parameter setup slightly increased the performance, going from 1 true positive and 133 false negatives in the original setup to 5 true positives, 3 false positives and 126 false negatives.

Files

OLAF.pdf
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