Date of Publication

2-22-2022

Document Type

Master's Thesis

Degree Name

Master of Science in Computer Science

Subject Categories

Databases and Information Systems | Theory and Algorithms

College

College of Computer Studies

Department/Unit

Computer Science

Thesis Advisor

Rafael A. Cabredo

Defense Panel Chair

Ronald M. Pascual

Defense Panel Member

Ronald M. Pascual
Rafael A. Cabredo
Ryan Austin Fernandez

Abstract/Summary

Audio fingerprinting techniques are commonly used to programmatically generate unique, compact digital signatures for songs. Given a fingerprint database of substantial size, these algorithms are capable of identifying a plethora of songs across a wide range of genres and languages based on a few short, contiguous seconds of auditory input. Existing studies point toward the use of audio fingerprinting algorithms for content-based audio identification. However, little is known about the relative performance of these algorithms when the audio file input has been intentionally tampered with, as in the case of audio modification for purposes of either unjust duplication or copyright infringement. On that premise, the goal of this study is to provide a comparative analysis of the performance be- tween two audio fingerprinting techniques, namely the Shazam and Quad-Based (Qfp) algorithms, as applied to the task of modified content-based audio identification. Performance indicators show that the Qfp algorithm outperforms the Shazam algorithm across all standard and modified audio identification tests on a collection of distorted audio samples built from high-quality music files. More studies are needed before drawing conclusions about the most ideal content-based fingerprinting approach to modified audio identification, and perhaps integrity verification tasks with mixed-signal audio queries.

Abstract Format

html

Language

English

Format

Electronic

Physical Description

xv, 175 leaves

Keywords

Information storage and retrieval systems—Fingerprints

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Embargo Period

2-15-2022

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