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CLC number: TN912.34

On-line Access: 2011-10-08

Received: 2010-11-16

Revision Accepted: 2011-03-18

Crosschecked: 2011-09-01

Cited: 2

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Journal of Zhejiang University SCIENCE C 2011 Vol.12 No.10 P.836-845

http://doi.org/10.1631/jzus.C1000396


An algorithm that minimizes audio fingerprints using the difference of Gaussians


Author(s):  Myoungbeom Chung, Ilju Ko

Affiliation(s):  Department of Media, Soongsil University, Seoul 156-743, Korea

Corresponding email(s):   nzin@ssu.ac.kr, andy@ssu.ac.kr

Key Words:  Audio retrieval, Audio fingerprint, Audio signal processing, Difference of Gaussians (DoG)


Myoungbeom Chung, Ilju Ko. An algorithm that minimizes audio fingerprints using the difference of Gaussians[J]. Journal of Zhejiang University Science C, 2011, 12(10): 836-845.

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Abstract: 
Recently, many audio search sites headed by Google have used audio fingerprinting technology to search for the same audio and protect the music copyright using one part of the audio data. However, if there are fingerprints per audio file, then the amount of query data for the audio search increases. In this paper, we propose a novel method that can reduce the number of fingerprints while providing a level of performance similar to that of existing methods. The proposed method uses the difference of Gaussians which is often used in feature extraction during image signal processing. In the experiment, we use the proposed method and dynamic time warping and undertake an experimental search for the same audio with a success rate of 90%. The proposed method, therefore, can be used for an effective audio search.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Baluja, S., Covell, M., 2007. Audio Fingerprinting: Combining Computer Vision & Data Stream Processing. Proc. Int. Conf. on Acoustics, Speech, and Signal Processing, p.213-216.

[2]Baluja, S., Covell, M., 2008. Waveprint: efficient wavelet-based audio fingerprinting. Pattern Recogn., 41(11):3467-3480.

[3]Brown, J.C., Hodgins-Davis, A., Miller, P.J.O., 2006. Classification of vocalizations of killer whales using dynamic time warping. J. Acoust. Soc. Am., 119(3):EL34-EL40.

[4]Cano, P., Batlle, E., Kalker, T., Haitsma, J., 2005. A review of audio fingerprinting. J. VLSI Signal Process., 41(3):271-284.

[5]Chung, M.B., Ko, I.J., 2010. Identical-video retrieval using the low-peak feature of a video’s audio information. J. Zhejiang Univ.-Sci. C (Comput. & Electron.), 11(3):151-159.

[6]Enswers Co., 2008. Method and Apparatus for Generating Audio Fingerprint Data and Comparing Audio Data Using the Same (10-2008-0098878). Korean Intellectual Property.

[7]Independent Recording Network, 2006. Interactive Frequency Charts. Available from http://www.independentrecording.net/irn/resources/freqchart/main_display.htm [Accessed on Sept. 2010].

[8]Kennedy, J., 2009. Digital Music Report 2009. IFPI. Available from http://www.ifpi.org/content/library/dmr2009.pdf [Accessed on Sept. 2010].

[9]Kim, S., Unal, E., Narayanan, S., 2008. Music Fingerprint Extraction for Classical Music Cover Song Identification. Proc. IEEE Int. Conf. on Multimedia and Expo, p.1261-1264.

[10]Kimura, A., Kashino, K., Kurozumi, T., Murase, H., 2001. Very Quick Audio Searching: Introducing Global Pruning to the Time-Series Active Search. Proc. IEEE Int. Conf. on the Acoustics, Speech, and Signal Processing, p.1429-1432.

[11]Logan, B., 2000. Mel Frequency Cepstral Coefficients for Music Modeling. Proc. Int. Symp. on Music Information Retrieval.

[12]Lowe, D.G., 2004. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis., 60(2):91-110.

[13]Mihak, M., Venkatesan, R., 2001. A perceptual audio hashing algorithm: a tool for robust audio identification and information hiding. LNCS, 2137:51-65.

[14]Mikolajczyk, K., Schmid, C., 2004. Scale & affine invariant interest point detectors. Int. J. Comput. Vis., 60(1):63-86.

[15]Pickens, J., 2002. A Comparison of Language Modeling and Probabilistic Text Information Retrieval Approaches to Monophonic Music Retrieval. Proc. Int. Symp. on Music Information Retrieval.

[16]Ponte, J.M., Croft, W.B., 1998. A Language Modeling Approach to Information Retrieval. Proc. 21st Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, p.275-281.

[17]Rein, S., Reisslein, M., 2006. Identifying the classical music composition of an unknown performance with wavelet dispersion vector and neural nets. Inf. Sci., 176(12):1629-1655.

[18]Stephen, D.J., 1999. Music Retrieval as Text Retrieval: Simple yet Effective. Proc. 22nd Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, p.297-298.

[19]Wang, A.L.C., Smith, J.O., 2002. Method for Search in an Audio Database. World Intellectual Property Organization Publication WO/2002/11123A2. Available from http://www.wipo.int/pctdb/en/wo.jspIA=WO2002011123 [Accessed on Sept. 2010].

[20]Wold, E., Blum, T., Keislar, D., Wheaton, J., 1996. Content-based classification, search, and retrieval of audio. IEEE Multimedia, 3(3):27-36.

[21]Youssef, A.M., Abdel-Galil, T.K., El-Saadany, E.F., Salama, M.M.A., 2004. Disturbance classification utilizing dynamic time warping classifier. IEEE Trans. Power Del., 19(1):272-278.

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