An Efficient Music Identification System Based on PostgreSQL User-Defined Functions
Main Article Content
Abstract
Abstract. In this paper, we present a novel approach for music identification task aimed at proving the ability to identify a song by recorded song snippets. By combining Y. Ke’s feature extracting method [1, 2] with PostgreSQL user-defined functions [3, 4, 5]], our system proves as an effective search strategy for the field. We construct training data sets in a noisy environment and compare the search speed and the search accuracy of the system with Y. Ke’s system. Experiment results show that our system is more powerful with the accurate retrieval ability of 98% on a database of 600 songs and the search speed is 3.6 times faster than Y. Ke’s system.
Keywords: Audio snippet, music identification, user-defined functions.
References
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[16] http://www.fftw.org
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[18] http://www.mpg123.de
[2] Y.Ke et al., Computer vision for music identification: server code,
[3] Nei Matthew and Richard Stones, Beginning Databases with PostgreSQL: From Novice to Professional, Second Edition, 2005.
[4] Korry Douglas, Susan Douglas, The comprehensive guide to building, programming, and administering PostgreSQL databases, Second Edition, 2005.
[5] http://www.postgresql.org/docs/8.0/static
[6] http://www.shazam.com
[7] http://www.relatable.com
[8] http://www.musipedia.org
[9] http://www.napster.com
[10] J. Haitsma, T. Kalker, A Highly Robust Audio Fingerprinting System, Proceedings of the International Conference for Music Information Retrieval, 2002.
[11] J. Haitsma, T. Kalker, J. Oostveen, Robust Audio Hashing for Content Identification, Content Based Multimedia Indexing 2001, Brescia, Italy, 2001.
[12] M. Fischler and R. Bolles. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography In Communications of the ACM, 24(6), 1981.
[13] S. Baluja, M. Covell, Content fingerprinting using wavelets, Proceedings of the 3rd European Conference on Visual Media Production (CVMP), 2006.
[14] S. Baluja and M. Covell, Audio Fingerprinting: Combining Computer Vision & Data Stream Processing, Proceeding of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2007.
[15] M. Covell, S. Baluja, Known-Audio Detection Using Waveprint: Spectrogram Fingerprinting By Wavelet Hashing, Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2007.
[16] http://www.fftw.org
[17] http://www.ffmpeg.org
[18] http://www.mpg123.de