Browse > Article

Classification of General Sound with Non-negativity Constraints  

조용춘 (삼성전자 DM총괄 DM연구소)
최승진 (포항공과대학교 컴퓨터공학과)
방승양 (포항공과대학교 컴퓨터공학과)
Publication Information
Journal of KIISE:Software and Applications / v.31, no.10, 2004 , pp. 1412-1417 More about this Journal
Abstract
Sparse coding or independent component analysis (ICA) which is a holistic representation, was successfully applied to elucidate early auditor${\gamma}$ processing and to the task of sound classification. In contrast, parts-based representation is an alternative way o) understanding object recognition in brain. In this thesis we employ the non-negative matrix factorization (NMF) which learns parts-based representation in the task of sound classification. Methods of feature extraction from the spectro-temporal sounds using the NMF in the absence or presence of noise, are explained. Experimental results show that NMF-based features improve the performance of sound classification over ICA-based features.
Keywords
Sound Classification; Non-negative Matrix Factorization; Audio signal processing;
Citations & Related Records
연도 인용수 순위
  • Reference
1 B. A. Olshausen and D. J. Field, 'Sparse coding with an overcomplete basis set: A strategy employed by V1,' Vision Research, Vol. 37, pp. 3311-3325, 1997   DOI   ScienceOn
2 D. A. Depireux, J. Z. Simon, D. J. Klein, and S. A. Shamma, 'Spectro-ternporal response field characterization with dynamic ripples in ferret primary auditory cortex,' J. Neuro-physiology, Vol. 85, pp. 1220-1234, 2001
3 S. Shamma, 'On the role of space and time in auditory processing,' TRENDS in Cognitive Science, Vol. 5, No.8, pp. 340-348, 2001   DOI   ScienceOn
4 M. S. Gazzaniga, R. B. Ivry, and G. R. Mangum, Cognitive Neuroscience: The Biology of the Mind, W. W. Norton & Company, New York, 2001
5 D. D. Lee and H. S. Seung, 'Algorithms for non-negative matrix factorization,' in Advances in Neural Information Processing Systems, Vol. 13, 2001
6 L. R. Rabiner and B. H. Juang, 'An introduction to hidden Markov models,' IEEE trans, Acoustics, Speech and Signal Processing Magazine, Vol 3, pp. 4-16, 1986
7 M. Casey, 'Sound classification and similarity tools,' in Introduction to MPEG-7: Multimedia Content Description Language, B. S. Manjunath, P. Salembier, and T. Sikora, Eds. John Wiley & Sons, Inc., 2001
8 M. Casey, 'Reduced-rank spectra and minimum-entropy priors as consistent and reliable cues for generalized sound recognition,' in Proc. Workshop on Consistent and Reliable Acoustic Cues for Sound Analysis, Eurospeech, Aalborg, Denmark, 2001
9 A. Hyvsrinen, J. Karhunen, and E. Oja, Independent Component Analysis, John Wiley & Sons, Inc., 2001
10 A. Cichocki and S. Amari, Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications, John Wiley & Sons, Inc., 2002
11 A. Bell and T. Sejnowski, 'Learning the higherorder structure of a natural sound,' Network: Computation in Neural Systems, Vol. 7, pp. 261-266, 1996   DOI   ScienceOn
12 M. S. Lewicki, 'Efficient coding of natural sounds,' Nature Neuroscience, Vol. 5, No.4, pp. 356- 363, 2002   DOI   ScienceOn
13 K. P. Kording, P. Konig, and D. J. Klein, 'Learning of sparse auditory receptive fields,' in Proc. IJCNN, Honolulu, Hawaii, 2002   DOI
14 D. D. Lee and H. S. Seung, 'Learning the parts of objects by non-negative matrix factorization,' Nature, Vol. 40, pp. 788-791, Oct. 1999   DOI   ScienceOn