Acoustic environment classification

ACM Transactions on Speech and Language Processing

Volumn 3, Issue 2, 2006, Pages 1-22

  Ma, Ling ^a   Milner, Ben ^b   Smith, Dan ^b  

^a UNIVERSITY OF YORK   (United Kingdom)

^b UNIVERSITY OF EAST ANGLIA   (United Kingdom)

Author keywords

Sound classification

Indexed keywords

ACOUSTIC SIGNAL PROCESSING; ADAPTIVE SYSTEMS; HIERARCHICAL SYSTEMS; LEARNING SYSTEMS; MICROPHONES; SOFTWARE PROTOTYPING;

COMMUNICATION MODES; CONSUMER DEVICES; HIERARCHICAL CLASSIFICATION MODELS; SOUND CLASSIFICATION;

CLASSIFICATION (OF INFORMATION);

EID: 33746905459     PISSN: 15504875     EISSN: 15504883     Source Type: Journal    
DOI: 10.1145/1149290.1149292     Document Type: Review

References (37)

1. BAKKER, E. M. AND LEW, M. S. 2002. Semantic retrieval using audio analysis. In Proceedings of the Conference on Image and Video Retrieval. London UK. Lecture Notes in Computer Science, vol. 2383. 271-277.
2. BROWNE, P., CZIRJEK, C., GURRIN, C., JARINA, R., LEE, H., MARLOW, S., MCDONALD, K., MURPHY, N., O'CONNOR, N. E., SMEATON, A. F., AND YE, J. 2003. Dublin City University video track experiments for TREC 2002.
3. CAI, R., LU, L., ZHANG, H. J., AND CAI, L.-H. 2003. Using structure patterns of temporal and spectral feature in audio similarity measure. In Proceedings of the ACM Multimedia Conference. Berkeley, CA. (Nov.). 219-222.
4. CLARKSON, B., SAWHNEY, N., AND PENTLAND, A. 1998. Auditory context awareness via wearable computing. Workshop on Perceptual User Interfaces. 37-42.
5. COUVREUR, L. AND LANIRAY, M. 2004. Automatic noise recognition in urban environments based on artificial neural networks and hidden Markov models. Inter-noise2004. Prague, Czech Republic.
6. DUDA, R. O., HART, P. E., AND STORK, D. G. 2001. Pattern Classification, 2nd Ed. Wiley, New York, NY.
7. FOOTE, J. 1999. An overview of audio information retrieval. Multimedia Syst. 7, 1, 2-11.
8. GAUNARD, P., MUBIKANGIEY, C. G., COUVREUR, C., AND FONTAINE, V. 1998. Automatic classification of environmental noise events by hidden Markov models. Appl. Acoustics 54, 3, 187.
9. HINDUS, D. AND SCHMANDT, C. 1992. Ubiquitous audio: Capturing spontaneous collaboration. In Proceedings of Computer-Supported Cooperative Work (CSCW). Toronto, Canada (Nov.). 210-217.
10. HUANG, X., ACERO, A., AND HON, H. 2001. Spoken Language Processing. Prentice Hall, Englewood Cliffs, NJ.
11. LEGGETTER, C. J. AND WOODLAND, P. C. 1995. Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models. Comput. Speech Lang. 9, 171-185.
12. LIU, L., JIANG, H., AND ZHANG, H.-J. 2001. A robust audio classification and segmentation method. In Proceedings of the ACM Multimedia Conference. Ottawa, Canada. 203-211.
13. LIU, L., ZHANG, H.-J., AND JIANG, H. 2002. Content analysis for audio classification and segmentation. IEEE Trans. Speech Audio Process. 10, 7, 504-516.
14. MA, L., SMITH, D. J., AND MILNER, B. P. 2003. Context awareness using environmental noise classification. In Proceedings of Eurospeech. Geneva, Switzerland, 2237-2240.
15. MA, L., SMITH, D. J., AND MILNER, B. P. 2003. Environmental noise classification for context-aware applications. In Proceedings of the International Conference on Database and Expert Systems Applications (DEXA). Lecture Notes in Computer Science, vol. 2736. 360-370.
16. MYNATT, E. D., BACK, M., WANT, R., BAER, M., AND ELLIS, J. B. 1998. Designing audio aura. In Proceedings of Conference on Human Factors in Computing Systems (CHI'98). 566-573.
17. NISHIURA, T., NAKAMURA, S., MIKI, K., AND SHIKANO, K. 2003. Environment sound source identification based on hidden Markov model for robust speech recognition. In Proceedings of EuroSpeech. 2157-2160.
18. PELTONEN, V. T. K., ERONEN, A. J., PARVIAINEN, M. P., AND KLAPURI, A. P. 2001. Recognition of everyday auditory environments: Potentials, latencies and cues, 110th Convention of Audio Engineering Society.
19. PELTONEN, V., TUOMI, J., KLAPURI, A., HUOPANIEMI, J., AND SORSA, T. 2002. Computational auditory environment recognition. In Proceedings of the International Conference on Acoustic, Speech, and Signal Processing. Orlando, FL.
20. QUÉNOT, G. M., MORARU, D., BESACIER, L., AND HULHEM, P. 2003. CLIPS at TREC-11: Experiments in video retrieval. TREC-2002.
21. SAWHNEY, N. AND SCHMANDT, C. 2000. Nomadic radio: Speech and audio interaction for contextual messaging in nomadic environments. ACM Trans. Comput. Human. Interact. 7, 3, 353-383.
22. SAWHNEY, N. 1997. Situational awareness from environmental sounds. Tech. rep. for Modeling Adaptive Behavior (MAS 738). MIT Media Lab.
23. SCHEIRER, E. AND SLANEY, M. 1997. Construction and evaluation of a robust multifeature speech/music discriminator. In Proceedings of the International Conference on Acoustics, Speech, and Signal Processing. 1331-1334.
24. SCHMANDT, C., MARMASSE, N., MARTI, S., SHAWHNEY, N., AND WHEELER, S. 2000. Everywhere messaging. IBM Syst. J. 39, 3-4, 660-677.
25. SMEATON, A. P. AND OVER, P. 2003. TRECVID: Benchmarking the effectivenss of information retrieval tasks on digital video. Lecture Notes in Computer Science, vol. 2728. 19-27.
26. SMITH, D., MA, L., AND RYAN, N. 2005. Acoustic environment as an indicator of social and physical context. Person. Ubiquitous Comput. 10, 1 (DOI: 10.1007/s00779-005-0045-4).
27. SRINIVASEN, S., PETKOVIC, D., AND PONCELON, D. B. 1999. Towards robust features for classifying audio in the Cue Video system. In Proceedings of the ACM Multimedia Conference. 393-340.
28. STÄGER, M., LUKOWITZ, P., AND TRÖSTER, G. 2004. Implementation and evaluation of a low-power sound-based user activity recognition system. International Semantic Web Conference. 138-141.
29. STEWARD, J. 2005. Using a PDA for audio capture. BSc Project, University of East Anglia, Norwich, UK.
30. TOYODA, Y., HUANG, J., DING, S., AND LIU, Y. 2004. Environmental sound recognition by multilayered neural networks. In Proceedings of the 4th International Conference on Computer and Information Technology (CIT'04). 123-127.
31. TZANETAKIS, G. AND COOK, P. 2002. Musical genre classification of audio signals. IEEE Trans. Speech Audio Process. 10, 5, 293-302.
32. VEGA, V. S B., BRESSAN, S. 2003. Continuous naive bayesian classifications. In Proceedings of the International Conference on Asian Digital Libraries. 279-289.
33. VENDRIG, J., DEN HARTOG, J., VAN LEEUWEN, D., PATRAS, I., RAAIJMAKERS, S., VAN REST, J., SNOEK, C., AND WORRING, M. 2003. TREC feature extraction by Active learning, TREC-2002.
34. WOLD, E., BLUM, T., KESLAR, D., AND WHEATON, J. 1996. Content-based classification search and retrieval of audio. IEEE Multimedia 3, 3, 27-36.
35. WU, L., GUO, Y., QIU, X., FENG, Z., RONG, J., JIN, W., ZHOU, D., WANG, R., AND JIN, M. 2003. TRECVid 2003. TREC-2003.
36. YOUNG, S., EVERMANN, G., KERSHAW, D., MOORE, G., ODELL, J., OLLASON, D., VALTCHEV, V., AND WOODLAND, P. 2001. The HTK Book 3.1. Cambridge University Engineering Department, Cambridge, UK. http://htk.eng.cam.ac.uk.
37. ZHUANG, L., ZHOU, F., AND TYGER, J. D. 2005. Keyboard acoustic emanations revisited. In Proceedings of the ACM Conference on Computer and Communications Security. Alexandria, VA (Nov).