• 전자공학회논문지 IE
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• v.47 no.4
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• pp.10-17
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• 2010

There is no general method to find out from signals of the channel outputs of ICA(Independent Component Analysis) which is what you want. Assuming speech signals contaminated with the sound from the muffler of a car, this paper presents the method which shows what you want, It is anticipated that speech signals will show larger correlation coefficients for speech signals than others. Batch, maximum and average method were proposed using 'ah', 'oh', 'woo' vowels whose signals were spoken by the same person who spoke the speech signals and using the same vowels whose signals are by another person. With the correlation coefficients which were calculated for each vowel, voting and summation methods were added. This paper shows what the best is among several methods tried.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.164-167
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• 2012

The technology which is used for segregating voices signals from exhaust noise signals of a car is very practical one to realize the interfaces between men and machines using voices. The voice signals contaminated by exhaust noise signal of a car was separated by canonical correlation ananysis(CCA) in an environment which does not guarantee the independence between signals and have prior informations. Rearrangement for the input signals is important in CCA. CCA was studied and segragation between source signals were performed by CCA through rearrangements of each of signals. It is possible to apply the technique to various signals since it is also possible to use CCA to the signals which are not independent.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.10
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• pp.2093-2099
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• 2011

Speech signal and car noise signal such as muffler noise are segregated from the one which has both signals mixed using statistical method. To classify speech signal from the other in segregated signals, FFT coefficients were obtained for all segments of a signal where each segment consists of 128 elements of a signal. For several coefficients of FFT corresponding to the low frequencies of a signal, autocovariances are calculated between coefficients of same order of all segments of a signal. Then they were averaged over autocovariances. Linear equation was eatablished for the those autocovariances using the linear regression method for each siganl. The coefficient of the slope of the line gives reference to compare and decide what the speech signal is. It is what this paper proposes. The results show it is very useful.

• 전자공학회논문지 IE
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• v.49 no.2
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• pp.24-29
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• 2012

The sounds of exhaust emissions of automobiles are independent sound sources which are nothing to do with voices. We have no information for the sources of voices and exhaust sounds. Accordingly, Independent Component Analysis which is one of the Blind Source Separaton methods was used to segregate two source signals from each mixed signals. Maximum Likelyhood Estimation was applied to the signals came through the stereo microphone to segregate the two source signals toward the maximization of independence. Since there is no clue to find whether it is speech signal or not, the coefficients of the slope was calculated by the autocovariances of the signals in frequcency domain. Noise remover for speech signals was implemented by coupling the two algorithms.

• Journal of the korean Society of Automotive Engineers
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• v.16 no.5
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• pp.1-9
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• 1994

여기에서는 승용차의 구성부품별 해석기술의 분야별로 기술동향을 찾아보고자 한다. 1. 엔진과 변속장치 분야. 1.1 파워 플랜트 강성. 1.2 음원대책. 1.3 미션분야. 2. 흡.배기계 분야. 2.1 흡.배기음. 2.2 실내소음. 3. 엔진 마운팅 분야. 3.1 마운팅 레이아웃. 3.2 마운팅 특성. 3.3 전자제어 마운팅. 4. 타이어.서스펜션 분야. 4.1 서스펜션. 4.2 타이어. 5. 보디,프레임 분야. 5.1 보디구조. 5.2 실내음. 5.3 풍절은과 그외의 음. 5.4 경량 고강성. 6. 진동.소음실험 기술분야. 6.1 계측기술. 6.2 센서. 6.3 실험 시뮬레이션. 6.4 감성의 정량화. 7. 시뮬레이션 해석 분야. 7.1 적용범위. 7.2 복합 시뮬레이션. 7.3 모델화. 7.4 감성 시뮬레이션.