[논문]스마트 시티에서의 이머전시 사운드 감지방법

조영임

doi:10.5302/j.icros.2010.16.12.1143

[국내논문] 스마트 시티에서의 이머전시 사운드 감지방법
A Emergency Sound Detecting Method for Smarter City 원문보기

제어·로봇·시스템학회 논문지 = Journal of institute of control, robotics and systems, v.16 no.12, 2010년, pp.1143 - 1149

Abstract ▼ AI-Helper

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.

주제어

AI 본문요약
AI-Helper

* AI 자동 식별 결과로 적합하지 않은 문장이 있을 수 있으니, 이용에 유의하시기 바랍니다.

문제 정의

The goal of this paper is that to detect the emergency sound and make someone who it is concerned with handled the situation immediately without hesitation.
So, this paper is about how to detect many emergency types of sound in real city life, especially on CCTV. This paper is for implementing the smarter city by detecting many types of sounds and filtering one of the emergency sound among the sound stream. And then it can be possible to deal with the dangerous or emergency situation.

제안 방법

Based on the flat lexicon and the lexical tree, the speech recognition system built in this work is designed for speedy interpretation of the voice data and is connected to CCTV's for visual in situ inspection [8].
Thus, it can be designed using the device transfer functions and the mathematical expressions. Within the scope of this research, the physical states, such as operational stability and sensitivity and the safe transmission of data, are assumed to be steady, and the main priority is placed on minimizing the number of devices and increasing the speed of the filter operation.

대상 데이터

To test the speech recognition system developed thus far in this work, speaking word database developed by SITEC is used. The database is recorded in 16khz/16 bit, and contained the voices of 500 individuals. For comparison to the database, voice data from a microphone or CCTV in 16khz/16 bit format are used.
그림10. 시뮬레이션에서 사용된 bigram과 trigram모델.

성능/효과

By comparing the noise-free speeches processed through the MATLAB-constructed Wiener filter, as illustrated in Fig. 1, to the existing database of key words, a very accurate speech recognition effect was realized (Fig. 9).

후속연구

The basic premise is to selectively use the audio signal being transmitted from the CCTV's. That is, from the transmitted signal, only the audio energy spectrum that is relevant to the speech is to be selected, digitized, and saved for further analysis. A high-performance FIR Wiener filer can be used to digitally filter out the unwanted portion of the audio signal, prior to actual speech recognition.

참고문헌 (15)

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.

상세보기
K. Nicos, Intelligent Cities: Innovation, Knowledge Systems and Digital Spaces. London: Spon Press, 2002
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.

상세보기
H. Kruegle, “CCTV surveillance,” Analog and Digital Video Practices and Technology, Elsevier, pp. 227-239, 2007.
Y. Gong, “Speech recognition in noisy environments: a survey,” Speech Communication, vol. 16, no. 3, pp. 261-291, Apr. 1995.

상세보기
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.

상세보기
K. S. Kim, “MATLAB signal and image processing,” Ajin Publishing, Korea, pp. 213-250, 2007.
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.

원문보기 상세보기
J. K. Kim, “Min/Max estimation and base esitmation for Kalman filter,” Natural Science Research (Korean), vol. 5, pp. 21-30, 1995.
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.
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.
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.
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.
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.
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.

내보내기 구분	파일저장 인쇄 메일전송
구성항목	기본정보 상세정보 관리번호, 논문명, 저널/프로시딩명, 저자 , 발행년, 권, 호, 시작페이지, 끝페이지, 발행기관 관리번호, 논문명, 대등논문명, 저자 , 저널/프로시딩명, 발행기관, 발행년, 발행언어, 권, 호, 시작페이지, 끝페이지, ISBN, ISSN, 주제분야, 키워드, 초록(한글), 초록(영문), 저자(소속기관)
저장형식	Text(ASCII format) Excel format RefWorks Direct Export RIS format (for Reference Manager, ProCite, EndNote), Scholar's Aids, Mendeley
메일정보	받는사람 (필수) @ 보내는사람 (선택) @ 제목 내용 KISTI 검색결과 이메일 서비스
안내	총 건의 자료가 검색되었습니다. 다운받으실 자료의 인덱스를 입력하세요. (1-10,000) 검색결과의 순서대로 최대 10,000건 까지 다운로드가 가능합니다. 데이타가 많을 경우 속도가 느려질 수 있습니다.(최대 2~3분 소요) 다운로드 파일은 UTF-8 형태로 저장됩니다. 파일의 내용이 제대로 보이지 않을실 때는 웹브라우저 상단의 보기 -> 인코딩 -> 자동선택 여부를 확인하십시오. ~ Text(ASCII format) Excel format

연합인증

[국내논문] 스마트 시티에서의 이머전시 사운드 감지방법
A Emergency Sound Detecting Method for Smarter City 원문보기

Abstract ▼ AI-Helper

주제어

AI 본문요약
AI-Helper

문제 정의

제안 방법

대상 데이터

성능/효과

후속연구

참고문헌 (15)

이 논문을 인용한 문헌

저자의 다른 논문 :

관련 콘텐츠

원문 보기

원문 URL 링크

오픈액세스(OA) 유형

이 논문과 함께 이용한 콘텐츠

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

선택된 텍스트

연합인증

[국내논문] 스마트 시티에서의 이머전시 사운드 감지방법 A Emergency Sound Detecting Method for Smarter City 원문보기

Abstract ▼ AI-Helper

주제어

AI 본문요약 엑셀 다운로드 AI-Helper

문제 정의

제안 방법

대상 데이터

성능/효과

후속연구

참고문헌 (15)

이 논문을 인용한 문헌

저자의 다른 논문 :

조영임 (36)

관련 콘텐츠

원문 보기

원문 URL 링크

오픈액세스(OA) 유형

이 논문과 함께 이용한 콘텐츠

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

선택된 텍스트

[국내논문] 스마트 시티에서의 이머전시 사운드 감지방법
A Emergency Sound Detecting Method for Smarter City 원문보기

AI 본문요약
AI-Helper