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A Emergency Sound Detecting Method for Smarter City

스마트 시티에서의 이머전시 사운드 감지방법

  • 조영임 (수원대학교 컴퓨터학과)
  • Received : 2010.09.10
  • Accepted : 2010.12.01
  • Published : 2010.12.01

Abstract

Because the noise is the main cause for decreasing the performance at speech recognition, the place or environment is very important in speech recognition. To improve the speech recognition performance in the real situations where various extraneous noises are abundant, a novel combination of FIR and Wiener filters is proposed and experimented. The combination resulted in improved accuracy and reduced processing time, enabling fast analysis and response in emergency situations. Usually, there are many dangerous situations in our city life, so for the smarter city it is necessary to detect many types of sound in various environment. Therefore this paper is about how to detect many types of sound in real city, especially on CCTV. This paper is for implementing the smarter city by detecting many types of sounds and filtering one of the emergency sound in this sound stream. And then it can be possible to handle with the emergency or dangerous situation.

References

  1. M. Deakin, “From city of bits to e-topia: taking the thesis on digitally-inclusive regeneration full circle,” Journal of Urban Technology, vol. 14, no. 3, pp. 131-143. 2007. https://doi.org/10.1080/10630730801933119
  2. K. Nicos, Intelligent Cities: Innovation, Knowledge Systems and Digital Spaces. London: Spon Press, 2002
  3. J. Allen, D. Byron, M. Dzikovska, G. Ferguson, L. Galescu, and A. Stent, “Toward conversational human-computer interaction,” AI Magazine, vol. 22, no. 4, pp. 27-37, 2001.
  4. H. Kruegle, “CCTV surveillance,” Analog and Digital Video Practices and Technology, Elsevier, pp. 227-239, 2007.
  5. Y. Gong, “Speech recognition in noisy environments: a survey,” Speech Communication, vol. 16, no. 3, pp. 261-291, Apr. 1995. https://doi.org/10.1016/0167-6393(94)00059-J
  6. C.-H. Lee, “On stochastic feature and model compensation approaches to robust speech recognition,” Speech Communication, vol. 25, no. 1-3, pp. 29-47, Aug. 1998. https://doi.org/10.1016/S0167-6393(98)00028-4
  7. K. S. Kim, “MATLAB signal and image processing,” Ajin Publishing, Korea, pp. 213-250, 2007.
  8. Y. I. Cho and S. S. Jang, “Intelligent speech recognition system for CCTV surveillance,” Korea Journal of Intelligent Systems, vol. 19, no. 3, pp. 415-420, 2009. https://doi.org/10.5391/JKIIS.2009.19.3.415
  9. J. K. Kim, “Min/Max estimation and base esitmation for Kalman filter,” Natural Science Research (Korean), vol. 5, pp. 21-30, 1995.
  10. J. J. Kang, B. O. Kang, H. Y. Jung, H. Jung, and Y. K. Lee, “Long words speech recognition technology and trend,” Electronic Communication Trend Analysis (Korean), vol. 23, no. 1, pp. 70-76, 2008.
  11. S. Doclo, Rong Dong, T. J. Klasen, J. Wouters, S. Haykin, and M. Moonen, “Extension of the multi-channel Wiener filter with ITD cues for noise reduction in binaural hearing aids,” Applications of Signal Processing to Audio and Acoustics, vol. 16, no. 16, pp. 70-73, 2005.
  12. J. H. Jang, D. K. Kim, and N. S. Kim, “A new statistical method for speech recognition systems,” Telecommunications Review (Korean), vol. 15, no. 1, pp. 201-209, 2005.
  13. T. K. Ryu, K. H, Park, D. S. Hong, and C. O. Kang, “Channel estimation by sero-forcing method in the frequency region,” Korea Journal of Telecommunications, vol. 31, no. 1, pp. 38-47, 2006.
  14. Y. S. Park and J. H. Jang, “Echo filtering by soft decision in the frequency region,” Telecommunications Review (Korean), vol. 19, no. 5, pp. 837-844, 2009.
  15. R. E. Morley, Jr. Gray E. Christensen, T. J. Sullivan, Orly Kamin, “The Design of a bit-serial coprocessor to perform mulitiplication and divison on a massively parallel architecture,” in Proc IEEE, The 2nd Syposium on the Frontiers of Massively Parallel Computation, Farifax, USA, pp. 419-422, 1998.