Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

(162.2KB)
This is an Open Access publication published under CSC-OpenAccess Policy.
Audio Art Authentication and Classification with Wavelet Statistics
Joel Martin
Pages - 1 - 8     |    Revised - 05-04-2013     |    Published - 30-04-2013
Volume - 4   Issue - 1    |    Publication Date - April 2013  Table of Contents
MORE INFORMATION
KEYWORDS
Audio Classification, Feature Extraction, Musical Genre Classification, Wavelets.
ABSTRACT
An experimental computation technique for audio art authentication is presented. Specifically, the computational techniques used by painting/drawings art authentication are transformed from twodimensional (image) into one-dimensional (audio) methods. The statistical model consists of first and higher-order wavelet statistics. Classification is performed with a multi-dimensional scaled 3D visual model. The results from the analyses of music/silence discrimination, audio art authentication, genre classification, and audio fingerprinting are demonstrated.
CITED BY (0)  
1 Google Scholar
2 CiteSeerX
3 refSeek
4 Scribd
5 SlideShare
6 PdfSR
1 S. Lyu, D. Rockmore, H. Farid. “A Digital Technique for Art Authentication.” Proc Natl Acad Sci USA. 101(49): 17006-10. 2004.
2 E. S. Parris, M. J. Carey, H. Lloyd-Thomas, “A comparison of features for speech, music discrimination.” Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing. 1:149-152. 1999.
3 S. Pfeiffer, S. Fischer, W. Effelsberg, “Automatic audio content analysis.” Proc. of 4th ACM Multimedia Conference. 1:21-30. 1996.
4 E. Wold, T. Blum, D. Keislar, J. Wheaton. “Content-Based Classification, search, and Retrieval of Audio.” IEEE Multimedia. 3(3):27-36. 1996.
5 G. Tzanetakis, P. Cook. “Music genre classification of audio signals.” IEEE Transactions on Speech and Audio Processing. 10(5):293-302. 2002.
6 L. Lu, H. Jiang, H. J. Zhang. “A robust audio classification and segmentation method.” Proc.9th ACM Int. Conf. Multimedia. 1:203--211. 2001.
7 T. Li, G. Tzanetakis. “Factors in automatic musical genre classification of audio signals.” Proc.of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA).2003.
8 M. Shan, F. Kuo. “Music style mining and classification by melody.” Proc. IEEE International Conference on Multimedia and Expo. 1:97-100. 2002.
9 W. Chai, B. Vercoe. “Folk music classification using hidden Markov models.” Proc International Conference on Artificial Intelligence. 2001.
10 B. Matityaho, M. Furst. “Neural network based model for classification of music type.” Proc.18th Conv. Electrical and Electronic Engineers in Israel. 4.3.4/1-5. 1995.
11 L. Endelt, A. Cour-Harbo, “Wavelets for sparse representation of music.” Proc of Wedelmusic. 2004.
12 T. Lambrou, P. Kudumakis, R. Speller, M. Sandler, A. Linney. "Classification of audio signals using statistical features on time and wavelet transform domains.” Proc. IEEE ICASSP. 6:3621-24. 1998
13 T. Cox, M. Cox. Multidimensional Scaling. Chapman & Hall, London, 1994.
Mr. Joel Martin
Columbia University Department of Electrical Engineering New York, NY, 10027 - United States of America
jrm2107@hotmail.com