Model based clustering of audio clips using Gaussian mixture models

Proceedings of the 7th International Conference on Advances in Pattern Recognition, ICAPR 2009

Volumn , Issue , 2009, Pages 47-50

  Chandrakala, S ^a   Chandra Sekhar, C ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY MADRAS   (India)

Author keywords

[No Author keywords available]

Indexed keywords

BLIND SOURCE SEPARATION; CLUSTERING ALGORITHMS; COMMUNICATION CHANNELS (INFORMATION THEORY); HIDDEN MARKOV MODELS; MAGNETOSTRICTIVE DEVICES; MIXTURES; OBJECT RECOGNITION; TRELLIS CODES;

AUDIO CLIPS; DATA SETS; GAUSSIAN MIXTURE MODELS; LOG LIKELIHOODS; LONG DURATIONS; MATRIXES; MODEL BASED CLUSTERING METHODS; MODEL-BASED CLUSTERING; MODEL-BASED METHODS; PERCEPTUAL FEATURES; SIMILARITY SCORES; TRAJECTORY DATUM;

TRAJECTORIES;

EID: 63649121105     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICAPR.2009.92     Document Type: Conference Paper

References (13)

1. T.W. Liao, "Clustering of time series data - A survey," Pattern Recognition, vol. 38, pp. 1857-1874, Nov. 2005.
2. I. Cadez, S. Gaffney, and P. Smyth, "A general probabilistic framework for clustering individuals," in ACM SIGKDD, Boston, USA, August 2000, pp. 140-149.
3. A. Panuccio, M. Bicego, and V. Murino, "A hidden markov model based approach to sequential data clustering," LNCS, Springer, vol. 2396, pp. 734-742, 2002.
4. Y. Xiong and D. Yeung, "Mixtures of ARMA models for model based time series clustering," in Proc. IEEE Int. Conf. on Data Mining, Japan, December 2002, pp. 717-720.
5. A. J. Bagnall and G. J. Janacek, "Clustering time series from ARMA models with clipped data," in ACM SIGKDD, Seattle,USA, August 2004, pp. 49-58.
6. T. Jebara, Y. Song, and K. Thadani, "Spectral clustering and embedding with hidden Markov models," in Eighteenth European Conference on Machine Learning (ECML), Warsaw, Poland, September 2007, pp. 164-175.
7. J. Yin and Q. Yang, "Integrating HMM and spectral analysis for sensory time series clustering," in Fifth International Conference on Data Mining(ICDM), Houston, Texas, November 2005, pp. 506-513.
8. E. Pekalska and R. Duin, "Dissimilarity representations allow for building good classifiers," Pattern Recognition Letters, vol. 23, pp. 943-956, 2002.
9. R. Anitha, D. Srikrishna Satish, and C. Chandra Sekhar, "Outerproduct of trajectory matrix for acoustic modeling using support vector machines," in IEEE International Workshop on Machine Learning for Signal Processing, September 2004, pp. 355-363.
10. S. Chandrakala and C. Chandra Sekhar, "A density based method for multivariate time series clustering in kernel feature space," in Proc. Int. Joint Conf. Neural Networks, Hong Kong, June 2008, pp. 1886-1891.
11. A. Y. Ng, M. I. Jordan, and Y. Weiss, "On spectral clustering: Analysis and an algorithm," in NIPS 14, 2002, pp. 849-856.
12. M. Girolami, "Mercer kernel based clustering in feature space," IEEE Trans. Neural Networks, vol. 13, no. 3, pp. 780-784, 2002.
13. Anvita Bajpai, "Features for audio clip classification," M.S Thesis, Indian Institute of Technology Madras, India, 2004.