A physiologically inspired method for audio classification

Eurasip Journal on Applied Signal Processing

Volumn 2005, Issue 9, 2005, Pages 1374-1381

  Ravindran, Sourabh ^a,b   Schlemmer, Kristopher ^a,c   Anderson, David V ^a,d,e,f,g  

^a GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

^b IEEE   (United States)

^c Raytheon Integrated Defense Systems   (United States)

^d GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

^e ACOUSTICAL SOCIETY OF AMERICA   (United States)

^f Tau Beta Pi ^*  (United States)

^g AMERICAN SOCIETY FOR ENGINEERING EDUCATION   (United States)

Author keywords

Audio classification; Auditory model; Feature extraction; Gaussian mixture models; Neural nets

Indexed keywords

AUDIO CLASSIFICATION; AUDITORY MODEL; GAUSSIAN MIXTURE MODELS;

FEATURE EXTRACTION; MATHEMATICAL MODELS; NEURAL NETWORKS; STATISTICS;

SIGNAL PROCESSING;

EID: 27844503926     PISSN: 11108657     EISSN: None     Source Type: Journal    
DOI: 10.1155/ASP.2005.1374     Document Type: Article

References (16)

1. K. Wang and S. Shamma, "Self-normalization and noiserobustness in early auditory representations," IEEE Trans. Speech Audio Processing, vol. 2, no. 3, pp. 421-435, 1994.
2. S. Ravindran, D. Anderson, and M. Slaney, "Low-power audio classification for ubiquitous sensor networks," in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing (ICASSP '04), Montreal, Canada, May 2004.
3. A. Teolis and S. Shamma, "Classification of transient signals via auditory representations," Tech. Rep. TR 91-99, Systems Research Center, University of Maryland, College Park, Md, USA, 1991.
4. E. Kandel, J. Schwartz, and T. Jessel, Principles of Neural Science, McGraw-Hill, New York, NY, USA, 4th edition, 2000.
5. R. Berne and M. Levy, Physiology, Mosby, New York, NY, USA, 4th edition, 1998.
6. P. Denes and E. Pinson, The Speech Chain, Freeman, New York, NY, USA, 2nd edition, 1993.
7. C. Koch and I. Segev, Eds., Methods in Neural Modelling, MIT Press, Cambridge, Mass, USA, 1989.
8. X. Yang, K. Wang, and S. Shamma, "Auditory representations of acoustic signals," IEEE Trans. Inform. Theory, vol. 38, no. 2, pp. 824-839, 1992.
9. K. Wang and S. Shamma, "Spectral shape analysis in the central auditory system," IEEE Trans. Speech Audio Processing, vol. 3, no. 5, pp. 382-395, 1995.
10. L. Atlas and S. Shamma, "Joint acoustic and modulation frequency," EURASIP Journal on Applied Signal Processing, vol. 2003, no. 7, pp. 668-675, 2003.
11. J. O. Pickles, An Introduction to the Physiology of Hearing, Academic Press, New York, NY, USA, 2nd edition, 2000.
12. P. D. Smith, M. Kucic, R. Ellis, P. Hasler, and D. V. Anderson, "Mel-frequency cepstrum encoding in analog floatinggate circuitry," in Proc. IEEE Int. Symp. Circuits and Systems (ISCAS '02), vol. 4, pp. 671-674, Scottsdale, Ariz, USA, May 2002.
13. I. Nabney and C. Bishop, "Netlab neural network software," http://www.ncrg.aston.ac.uk/netlab/.
14. V. Peltonen, J. Tuomi, A. Klapuri, J. Huopaniemi, and T. Sorsa, "Computational auditory scene recognition," in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing (ICASSP '02), Orlando, Fla, USA, May 2002.
15. M. Goto, H. Hashiguchi, T. Nishimura, and R. Oka, "RWC music database: Music genre database and musical instrument sound database," in Proc. 4th International Conference on Music Information Retrieval (ISMIR '03), pp. 229-230, Baltimore, Md, USA, October 2003.
16. J. Duchene and S. Leclercq, "An optimal transformation for discriminant and principal component analysis," IEEE Trans. Pattern Anal. Machine Intell., vol. 10, no. 6, pp. 978-983, 1988.