A Highly Robust Audio Fingerprinting System
@inproceedings{Haitsma2002AHR, title={A Highly Robust Audio Fingerprinting System}, author={Jaap Haitsma and Ton Kalker}, booktitle={ISMIR}, year={2002} }
Imagine the following situation. [] Key Method By using the fingerprint of an unknown audio clip as a query on a fingerprint database, which contains the fingerprints of a large library of songs, the audio clip can be identified. At the core of the presented system are a highly robust fingerprint extraction method and a very efficient fingerprint search strategy, which enables searching a large fingerprint database with only limited computing resources.
714 Citations
A Highly Robust Audio Fingerprinting System With an Efficient Search Strategy
- Computer Science
- 2003
An audio fingerprinting system that uses the fingerprint of an unknown audio clip as a query on a fingerprint database, which contains the fingerprints of a large library of songs, the audio clip can be identified.
Evaluating musical fingerprinting systems
- Art
- 2012
Audio fingerprinting is a process that uses computers to analyse small clips of music recordings to answer a common question that people who listen to music often ask : "What is the name of that song…
An audio fingerprinting system for live version identification using image processing techniques
- Computer Science2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- 2014
An audio fingerprinting system that can deal with live version identification by using image processing techniques and Compact fingerprints are derived using a log-frequency spectrogram and an adaptive thresholding method, and template matching is performed using the Hamming similarity and the Hough Transform.
A Quick Review of Audio Fingerprinting
- Computer Science
- 2003
The ability to identify some piece of music, given just a few seconds of input to go on, is quite an astonishing feat, one that requires a complex level of math and the compilation of huge databases.
A robust audio fingerprinter based on pitch class histograms: applications for ethnic music archives
- Computer Science
- 2012
The new acoustic fingerprinting system, presented here, has some interesting features which makes it a valuable tool to manage ethnic music archives: the fingerprints are rather robust against pitch shift, tempo changes, several synthetic audio effects, and reversal of the audio.
Stochastic Model of a Robust Audio Fingerprinting System
- Computer ScienceISMIR
- 2004
The initial outlines of a model for an existing robust fingerprinting system are presented and the stochastic behavior of the system when the input signal is a stationary (stochastic) signal is described, in this paper the input is assumed to be white noise.
Audio Fingerprinting using Fractional Fourier Transform
- Computer Science
- 2015
Fractional Fourier Transform is used to generate an audio fingerprint using an audio clip given as an input query and compares with the fingerprint stored in the database.
A Robust and Time-Efficient Fingerprinting Model for Musical Audio
- Computer Science2006 IEEE International Symposium on Consumer Electronics
- 2006
The audio spectrum flatness (ASF) and the audio signature (AS) features of the MPEG-7 standard are made use, which are new to the audio feature family and have not been considered as much as other feature types.
Panako - A Scalable Acoustic Fingerprinting System Handling Time-Scale and Pitch Modification
- Computer ScienceISMIR
- 2014
A scalable granular acoustic fingerprinting system robust against time and pitch scale modification is presented, designed to be robust against pitch shifting, time stretching and tempo changes, while remaining scalable.
Gaussian Mixture Modeling Using Short Time Fourier Transform Features for Audio Fingerprinting
- Computer Science2005 IEEE International Conference on Multimedia and Expo
- 2005
This paper designs fingerprints addressing the above issues by modeling an audio clip by Gaussian mixture models using a wide range of easy-to-compute short time Fourier transform features such as Shannon entropy, Renyi entropy, spectral centroid, spectral bandwidth, spectral flatness measure, spectral crest factor, and Mel-frequency cepstral coefficients and shows that the audio fingerprints modeled using GMM are robust to distortions not used in training.
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Philips (audio fingerprinting) website <http://www.research.philips.com/InformationCenter/Global/ FArticleSummary.asp?lNodeId=927&channel=927&channelI d=N927A2568>
- Philips (audio fingerprinting) website <http://www.research.philips.com/InformationCenter/Global/ FArticleSummary.asp?lNodeId=927&channel=927&channelI d=N927A2568>
Audible Magic website <http://www.audiblemagic
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