• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.2
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• pp.254-259
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• 2011

For the emergency detecting in general CCTV environment of our daily life, the monitoring by only images through CCTV information occurs some problems especially in cost as well as man power. Therefore, in this paper, for detecting emergency state dynamically through CCTV as well as resolving some problems, we propose a detection and recognition method for emergency and non-emergency speech by GMM. The proposed method determine whether input speech is emergency or non-emergency speech by global GMM. If emergeny speech, local GMM is performed to classify the type of emergency speech. The proposed method is tested and verified by emergency and non-emergency speeches in various environmental conditions.

• The KIPS Transactions:PartB
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• v.16B no.2
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• pp.151-156
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• 2009

This paper proposes a Gaussian mixture model(GMM)-based music discrimination system for FM broadcasting. The objective of the system is automatically archiving music signals from audio broadcasting programs that are normally mixed with human voices, music songs, commercial musics, and other sounds. To improve the system performance, make it more robust and to accurately cut the starting/ending-point of the recording, we also added a post-processing module. Experimental results on various input signals of FM radio programs under PC environments show excellent performance of the proposed system. The fixed-point simulation shows the same results under 3MIPS computational power.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.981-984
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• 2010

In this paper, we used Support Vector Machines(SVMs) as the learning method, one of Artificial Neural Network, to segregated from the continuous speech into phonemes, an initial, medial, and final sound, and then, performed continuous speech recognition from it. A Decision boundary of phoneme is determined by algorithm with maximum frequency in a short interval. Speech recognition process is performed by Continuous Hidden Markov Model(CHMM), and we compared it with another phoneme segregated from the eye-measurement. From the simulation results, we confirmed that the method, SVMs, we proposed is more effective in an initial sound than Gaussian Mixture Models(GMMs).

• ETRI Journal
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• v.32 no.5
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• pp.801-809
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• 2010

This paper presents a statistical model-based noise suppression approach for voice recognition in a car environment. In order to alleviate the spectral whitening and signal distortion problem in the traditional decision-directed Wiener filter, we combine a decision-directed method with an original spectrum reconstruction method and develop a new two-stage noise reduction filter estimation scheme. When a tradeoff between the performance and computational efficiency under resource-constrained automotive devices is considered, ETSI standard advance distributed speech recognition font-end (ETSI-AFE) can be an effective solution, and ETSI-AFE is also based on the decision-directed Wiener filter. Thus, a series of voice recognition and computational complexity tests are conducted by comparing the proposed approach with ETSI-AFE. The experimental results show that the proposed approach is superior to the conventional method in terms of speech recognition accuracy, while the computational cost and frame latency are significantly reduced.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.2
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• pp.83-90
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• 2015

Almost of current Automatic Incident Detection(AID) algorithms involve the vulnerability that detects the traffic accident in open road or in tunnel as the traffic jam not as the traffic accident. This paper proposes the improved accident detection algorithm to enhance the detection probability based on accident detection algorithms applied in open roads. The improved accident detection algorithm provides the preliminary judgment of potential accident by detecting the stopped object by Gaussian Mixture Model. Afterwards, it measures the detection area is divided into blocks so that the occupancy rate can be determined for each block. All experimental results of applying the new algorithm on a real incident was detected image without error.

• The Journal of the Korea Contents Association
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• v.17 no.1
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• pp.137-144
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• 2017

Face detection is still a challenging task under severe face pose variations in complex background. This paper proposes an effective algorithm which can detect single or multiple faces based on skin color detection and depth information. We introduce Gaussian mixture model(GMM) for skin color detection in a color image. The depth information is from three dimensional depth sensor of Kinect V2 device, and is useful in segmenting a human body from the background. Then, a labeling process successfully removes non-face region using several features. Experimental results show that the proposed face detection algorithm can provide robust detection performance even under variable conditions and complex background.

• Phonetics and Speech Sciences
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• v.7 no.1
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• pp.3-10
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• 2015

In this paper, different frequency scales in cepstral feature extraction are evaluated for the text-independent speaker recognition. To this end, mel-frequency cepstral coefficients (MFCCs), linear frequency cepstral coefficients (LFCCs), and bilinear warped frequency cepstral coefficients (BWFCCs) are applied to the speaker recognition experiment. In addition, the spectro-temporal features extracted by the cepstral-time matrix (CTM) are examined as an alternative to the delta and delta-delta features. Experiments on the NIST speaker recognition evaluation (SRE) 2004 task are carried out using the Gaussian mixture model-universal background model (GMM-UBM) method and the joint factor analysis (JFA) method, both based on the ALIZE 3.0 toolkit. Experimental results using both the methods show that BWFCC with appropriate warping factor yields better performance than MFCC and LFCC. It is also shown that the feature set including the spectro-temporal information based on the CTM outperforms the conventional feature set including the delta and delta-delta features.

• Journal of Platform Technology
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• v.11 no.5
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• pp.110-125
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• 2023

In this paper, we utilize time-series aircraft location data measured based on 2019 domestic airports to analyze Go-Around and UOC_D situations during the approach phase of domestic airports. Various clustering-based machine learning techniques are applied to determine the most appropriate analysis method for domestic aviation data through experimentation. The ADS-B sensor is solely employed to measure aircraft positions. We designed a model using clustering algorithms such as K-Means, GMM, and DBSCAN to classify abnormal situations. Among them, the RF model showed the best performance overseas, but through experiments, it was confirmed that the GMM showed the highest classification performance for domestic aviation data by reflecting the aspects specialized in domestic terrain.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.46-46
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• 2017

지역가뭄빈도분석의 분위산정에 대한 신뢰성은 수문학적으로 균일한 지역으로 구분하기 위해 사용된 장기간의 과거 자료와 분석절차에 의해 결정된다. 그러나 극심한 가뭄은 매우 드물게 발생하며 신뢰 할 수 있는 지역빈도분석을 위한 지속기간이 충분치 않는 경우가 많이 발생한다. 이 외에도 우리나라의 복잡한 지형적 및 기후적 특징은 동질한 지역으로 구분하기 위한 통계적인 처리방법이 필요하였다. 본 연구에서 적용한 지역빈도분석은 여러 지역의 다양한 변수인 수문기상 특성을 분석하여 동질한 지역을 확인하고, 주요 가뭄변수(지속 시간 및 심각도)를 통합 적용하여 각각의 동질한 지역 분위를 추정함으로써 동질한 지역을 구분하는 해결책을 제시하였다. 본 연구에서는 가우시안 혼합 모형(Gaussian Mixture Model)을 기반으로 기반 군집분석 방법을 적용하여 최적의 동질한 지역을 구분하고 그 결과를 우도비검정 및 다른 유효성 검사 지수를 이용해서 확인하였다. 가우시안 혼합 모델에서 산정했던 매개변수를 방향저감 공간으로 표현하기 위해서 가우시안 혼합 모델방향 저감(GMMDR)방법을 적용하였다. 이 변수는 가뭄빈도분석을 위해 다양한 분포와 코풀라(copula) 적합도를 이용하여 추정 비교하였다. 그 결과 우리나라를 4개의 동질한 지역으로 나누게 되었다. 가우시안과 Frank copula를 이용한 Pearson type III(PE3) 분포는 우리나라의 가뭄 기간과 심각도의 공동 분포를 추정하는데 적합한 것으로 나타났다.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.277-277
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• 2021

합류부는 인공수로 또는 자연하천에서 흔히 존재하며 매우 복잡한 흐름 구조가 발생하는 곳이다. 특히 본류와 지류의 유속장의 차이에 따라 발생하는 전단층은 흐름과 물질이 혼합되는 경계면이 되며, 흐름 구조가 전단층을 따라 발달한다는 특징으로 인해 수리학적으로 매우 중요하다. 최근 원격탐사 기법의 발전에 따라 위성이나 드론과 같은 무인 이동체를 이용한 하천 계측법이 수질 및 지형변화 연구들에 광범위하게 적용되고 있다. 그 중 RGB 항공영상은 해상도가 높고 취득 비용이 저렴하여 확장성 및 활용도가 높다. 본 연구에서는 합류부 전단층이 촬영된 RGB 항공 영상을 이용해 합류부 전단층 분석에 활용하는 방법을 제안한다. 제안되는 방법은 RGB 항공 영상에서 본류와 지류의 수체 영역을 각각 추출하기 위해 가우시안 혼합 모형(Gaussian mixture model)을 이용한다. 추출된 수체 영역에는 자기조직화지도(self-organizing map)을 적용하고 좌표 변환을 하여 정량적인 특징을 추출한다. 본 연구에서는 알고리듬의 적용 예로서 구글어스를 통해 확보된 낙동강-남강 합류부의 항공 영상을 분석한다. 본 추출법을 이용하면 접촉식 센서를 이용하는 기존의 전단층 계측 방법들에 비해 경제적이고 안전하며 합류부 흐름의 평면적 분석을 가능하게 할 수 있을 것으로 기대된다.