Acoustic detection of human activities in natural environments

AES: Journal of the Audio Engineering Society

Volumn 60, Issue 9, 2012, Pages 686-695

  Ntalampiras, Stavros ^a   Potamitis, Ilyas ^b   Fakotakis, Nikos ^c  

^a POLITECNICO DI MILANO   (Italy)

^b TECHNOLOGICAL EDUCATIONAL INSTITUTE OF CRETE   (Greece)

^c UNIVERSITY OF PATRAS   (Greece)

Author keywords

[No Author keywords available]

Indexed keywords

ACOUSTIC CONDITIONS; ACOUSTIC DETECTION; ACOUSTIC PARAMETERS; ACOUSTIC SURVEILLANCE; AUDIO SCENES; AUTOMATIC RECOGNITION; CONFUSION MATRICES; DESCRIPTORS; DETECTION CAPABILITY; DETECTION ERROR; FEED-BACK LOOP; HIERARCHICAL STRUCTURES; HUMAN ACTIVITIES; MODEL REFINEMENT; NATURAL ENVIRONMENTS; NATURAL SCENES; SITUATION ANALYSIS; TRADE-OFF CURVES; WAVELET DOMAIN;

HIDDEN MARKOV MODELS;

AUDIO ACOUSTICS;

EID: 84868584777     PISSN: 15494950     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (24)

1. D. Wang, and G. J. Brown, Computational Auditory Scene Analysis: Principles, Algorithms and Applications (Wiley-Blackwell, Oxford, 2006).
2. R. Bardeli, "Similarity Search in Animal Sound Databases," IEEE Transactions on Multimedia, vol. 11, no. 1, pp. 68-76 (2009 Jan.).
3. B. Defreville, F. Pachet, C. Rosin, P. Roy, "Automatic Recognition of Urban Sound Sources," presented at the 120th Convention of the Audio Engineering Society (2006 May), convention paper 6827.
4. K. Lopatka, A. Czyzewski, H. Krawczyk, "Automatic Recognition of Events in Audio Data Using Supercomputer Cluster," presented at the 130th Convention of the Audio Engineering Society (2011 May), convention paper 8377.
5. R. C. Maher and S. R. Shaw, "Directional Aspects of Forensic Gunshot Recordings," Proc. of Audio Engineering Society 39th Conference, Audio Forensics-Practices and Challenges, Hillerod, Denmark, (2010 June 17-19), conference paper 4-2.
6. S. Gunasekaran and K. Revathy, "Content-Based Classification and Retrieval of Wild Animal Sounds Using Feature Selection Algorithm," Second International Conference on Machine Learning and Computing (2010 Feb. 9-11), Bangladore, India, pp. 272-275.
7. W. Astuti, A. M. Aibinu, M. J. E Salami, R. Akmelawati, and Asan G. A Muthalif, "Animal Sound Activity Detection Using Multi-Class Support Vector Machines," 4th International Conference on Mechatronics (2011 May 17-19), Kuala Lumpur, Malaysia, pp. 1-5.
8. F. Weninger, and B. Schuller. "Audio Recognition in the Wild: Static and Dynamic Classification on a Real- World Database of Animal Vocalizations," International Conference on Acoustics, Speech and Signal Processing (2011 May 22-27), Prague, Czech Republic, pp. 337-340.
9. M. V. Ghiurcau, C. Rusu, and R.C. Bilcu, "Wildlife Intruder Detection Using Sounds Captured by Acoustic Sensors," IEEE International Conference on Acoustics, Speech and Signal Processing (2010 Mar. 14-19), Dallas, Texas, pp. 297-300.
10. M.V. Ghiurcau, C. Rusu, J. Astola and R. C. Bilcu, "Towards an Application for Detecting Intruders in Wildlife Regions," 18th European Signal Processing Conference (2010 Aug. 23-27), Aalborg, Denmark, pp. 1865- 1868.
11. S. Ntalampiras, I. Potamitis, and N. Fakotakis, "An Adaptive Framework for Acoustic Monitoring of Potential Hazards," EURASIP Journal on Audio, Speech and Music Processing, vol. 2009, doi:10.1155/2009/594103 (2009).
12. H.-G. Kim, N. Moreau and T. Sikora, MPEG-7 Audio and Beyond: Audio Content Indexing and Retrieval (Wiley Publishers, 2005).
13. S. Ntalampiras, I. Potamitis, and N. Fakotakis, "Exploiting Temporal Feature Integration for Generalized Sound Recognition," EURASIP Journal Advances in Signal Processing, vol. 2009, doi:10.1155/2009/807162 (2009).
14. C. Torrence and G. P. Compo, "A Practical Guide to Wavelet Analysis,"A Practical Guide to Wavelet Analysis," Bulletin of the American Meteorological society, vol. 79, no. 1, pp. 61-78 (1998).
15. D. F. Specht, "Generation of Polynomial Discriminant Functions for Pattern Recognition," IEEE Transactions on Electronic Computers, vol. 16, pp. 308-319 (1967 June).
16. F. Rosenblatt, "The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain," The Perceptron:AProbabilistic Model for Information Storage and Organization in the Brain," Psychological Review, vol. 65, pp. 386-408 (1958 Nov.).
17. V. N. Vapnik, The Nature of Statistical Learning Theory (Springer-Verlag, 1995).
18. G. Tzanetakis and P. Cook, "Musical Genre Classification of Audio Signals," IEEE Transactions on Speech and Audio Processing, vol. 10, no. 5, pp. 293-302 (2002 July). (Pubitemid 34950067)
19. L. P. Hansen, "Large Sample Properties of Generalized Method of Moments Estimation," Econometrica, vol. 50, pp. 1029-1054 (1982 July).
20. L. E. Baum and T. Petrie, "Statistical Inference for Probabilistic Functions of Finite State Markov Chains," Annals of Mathematical Statistics, vol. 37, pp. 1554-1563 (1966 Dec.).
21. H. G. Kim and T. Sikora, "Comparison of MPEG-7 Audio Spectrum Projection Features and MFCC Applied to Speaker Recognition, Sound Classification and Audio Segmentation," IEEE International Conference on Acoustics, Speech, and Signal Processing, Montreal, Canada (2004 May 17-21), pp. 925-928.
22. J. Sohn, N. S. Kim, and W. Sung, "A Statistical Model-Based Voice Activity Detection," IEEE Signal Processing Letters, vol. 6, no. 1, pp. 1-3 (1999 Jan.).
23. A. Martin, G. Doddington, T. Kamm, M. Ordowski, and M. Przybocki. "The DET Curve in Assessment of Detection Task Performance," 5th European Conference on Speech Communication and Technology (Eurospeech) (1997 Sep. 22-27), Rhodes, Greece, pp. 1895-1898.
24. D. A. Reynolds, T. F. Quatieri, and R. B. Dunn, "Speaker Verification Using Adapted Gaussian Mixture Models," Speaker Verification Using Adapted Gaussian Mixture Models," Digital Signal Processing, vol. 10, no. 1, pp. 19-41 (2000 Mar.). (Pubitemid 30592166)