A comparative analysis of audio fingerprinting techniques as applied to modified content-based audio identification

Clicks: 1
ID: 285495
2022
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #3,018 of 3,757 articles by views in Malay Journal

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 3,757 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
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.
Reference Key
persistent_1760655776_68f179a04223e Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Lachica, Daniel Philippe F.
Journal Malay Journal
Year 2022
DOI
DOI not found
URL
https://animorepository.dlsu.edu.ph/etdm_comsci/14
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.