• Title/Summary/Keyword: Gaussian mixture model

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Noise Rabust Speaker Verification Using Sub-Band Weighting (서브밴드 가중치를 이용한 잡음에 강인한 화자검증)

  • Kim, Sung-Tak;Ji, Mi-Kyong;Kim, Hoi-Rin
    • The Journal of the Acoustical Society of Korea
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    • v.28 no.3
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    • pp.279-284
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    • 2009
  • Speaker verification determines whether the claimed speaker is accepted based on the score of the test utterance. In recent years, methods based on Gaussian mixture models and universal background model have been the dominant approaches for text-independent speaker verification. These speaker verification systems based on these methods provide very good performance under laboratory conditions. However, in real situations, the performance of speaker verification system is degraded dramatically. For overcoming this performance degradation, the feature recombination method was proposed, but this method had a drawback that whole sub-band feature vectors are used to compute the likelihood scores. To deal with this drawback, a modified feature recombination method which can use each sub-band likelihood score independently was proposed in our previous research. In this paper, we propose a sub-band weighting method based on sub-band signal-to-noise ratio which is combined with previously proposed modified feature recombination. This proposed method reduces errors by 28% compared with the conventional feature recombination method.

Gunnery Classification Method using Shape Feature of Profile and GMM (Profile 형태 특징과 GMM을 이용한 Gunnery 분류 기법)

  • Kim, Jae-Hyup;Park, Gyu-Hee;Jeong, Jun-Ho;Moon, Young-Shik
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.48 no.5
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    • pp.16-23
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    • 2011
  • Muzzle flash based on gunnery is the target that has huge energy. So, gunnery target in a long range over xx km is distinguishable in the IR(infrared) images, on the other hand, is not distinguishable in the CCD images. In this paper, we propose the classification method of gunnery targets in a infrared images and in a long range. The energy from gunnery have an effect on varous pixel values in infrared images as a property of infrared image sensor, distance, and atmosphere, etc. For this reason, it is difficult to classify gunnery targets using pixel values in infrared images. In proposed method, we take the profile of pixel values using high performance infrared sensor, and classify gunnery targets using modeling GMM and shape of profile. we experiment on the proposed method with infrared images in the ground and aviation. In experimental result, the proposed method provides about 93% classification rate.

An Object Tracking Method for Studio Cameras by OpenCV-based Python Program (OpenCV 기반 파이썬 프로그램에 의한 방송용 카메라의 객체 추적 기법)

  • Yang, Yong Jun;Lee, Sang Gu
    • The Journal of the Convergence on Culture Technology
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    • v.4 no.1
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    • pp.291-297
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    • 2018
  • In this paper, we present an automatic image object tracking system for Studio cameras on the stage. For object tracking, we use the OpenCV-based Python program using PC, Raspberry Pi 3 and mobile devices. There are many methods of image object tracking such as mean-shift, CAMshift (Continuously Adaptive Mean shift), background modelling using GMM(Gaussian mixture model), template based detection using SURF(Speeded up robust features), CMT(Consensus-based Matching and Tracking) and TLD methods. CAMshift algorithm is very efficient for real-time tracking because of its fast and robust performance. However, in this paper, we implement an image object tracking system for studio cameras based CMT algorithm. This is an optimal image tracking method because of combination of static and adaptive correspondences. The proposed system can be applied to an effective and robust image tracking system for continuous object tracking on the stage in real time.

Speech synthesis using acoustic Doppler signal (초음파 도플러 신호를 이용한 음성 합성)

  • Lee, Ki-Seung
    • The Journal of the Acoustical Society of Korea
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    • v.35 no.2
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    • pp.134-142
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    • 2016
  • In this paper, a method synthesizing speech signal using the 40 kHz ultrasonic signals reflected from the articulatory muscles was introduced and performance was evaluated. When the ultrasound signals are radiated to articulating face, the Doppler effects caused by movements of lips, jaw, and chin observed. The signals that have different frequencies from that of the transmitted signals are found in the received signals. These ADS (Acoustic-Doppler Signals) were used for estimating of the speech parameters in this study. Prior to synthesizing speech signal, a quantitative correlation analysis between ADS and speech signals was carried out on each frequency bin. According to the results, the feasibility of the ADS-based speech synthesis was validated. ADS-to-speech transformation was achieved by the joint Gaussian mixture model-based conversion rules. The experimental results from the 5 subjects showed that filter bank energy and LPC (Linear Predictive Coefficient) cepstrum coefficients are the optimal features for ADS, and speech, respectively. In the subjective evaluation where synthesized speech signals were obtained using the excitation sources extracted from original speech signals, it was confirmed that the ADS-to-speech conversion method yielded 72.2 % average recognition rates.

New Scheme for Smoker Detection (흡연자 검출을 위한 새로운 방법)

  • Lee, Jong-seok;Lee, Hyun-jae;Lee, Dong-kyu;Oh, Seoung-jun
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.41 no.9
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    • pp.1120-1131
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    • 2016
  • In this paper, we propose a smoker recognition algorithm, detecting smokers in a video sequence in order to prevent fire accidents. We use description-based method in hierarchical approaches to recognize smoker's activity, the algorithm consists of background subtraction, object detection, event search, event judgement. Background subtraction generates slow-motion and fast-motion foreground image from input image using Gaussian mixture model with two different learning-rate. Then, it extracts object locations in the slow-motion image using chain-rule based contour detection. For each object, face is detected by using Haar-like feature and smoke is detected by reflecting frequency and direction of smoke in fast-motion foreground. Hand movements are detected by motion estimation. The algorithm examines the features in a certain interval and infers that whether the object is a smoker. It robustly can detect a smoker among different objects while achieving real-time performance.

Loitering Behavior Detection Using Shadow Removal and Chromaticity Histogram Matching (그림자 제거와 색도 히스토그램 비교를 이용한 배회행위 검출)

  • Park, Eun-Soo;Lee, Hyung-Ho;Yun, Myoung-Kyu;Kim, Min-Gyu;Kwak, Jong-Hoon;Kim, Hak-Il
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.21 no.6
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    • pp.171-181
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    • 2011
  • Proposed in this paper is the intelligent video surveillance system to effectively detect multiple loitering objects even that disappear from the out of camera's field of view and later return to a target zone. After the background and foreground are segmented using Gaussian mixture model and shadows are removed, the objects returning to the target zone is recognized using the chromaticity histogram and the duration of loitering is preserved. For more accurate measurement of the loitering behavior, the camera calibration is also applied to map the image plane to the real-world ground. Hence, the loitering behavior can be detected by considering the time duration of the object's existence in the real-world space. The experiment was performed using loitering video and all of the loitering behaviors are accurately detected.

Comparison of drone-based hyperspectral and multispectral imagery for bathymetry mapping (드론기반 초분광영상과 다분광영상을 활용한 수심산정 비교)

  • Yeonghwa Gwon;Dongsu Kim;Siyoon Kwon;Hojun You
    • Proceedings of the Korea Water Resources Association Conference
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    • 2023.05a
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    • pp.54-54
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    • 2023
  • 하천유역조사는 관련 법률의 규정에 의해 물관리정책의 수립에 필요한 기초정보를 제공하는 것을 목적으로 기본현황, 이수, 치수 환경생태 등 유역관리에 필요한 주요 조사항목을 대상으로 수행되고 있다. 조사방법 중 원격탐사자료 활용한 조사는 드론 모니터링 영상 및 위성영상자료를 이용해 댐·제방과 같은 치수 시설물의 안전관리, 수질 모니터링, 하천지형조사, 하상변동조사 등에 활용되고 있다. 최근에는 일반 RGB 영상뿐만 아니라 수백개의 분광밴드를 포함한 초분광영상을 이용한 하천조사 연구가 이루어지고 있다. 초분광영상은 분광해상도가 높아 다항목 조사에 활용할 수 있다는 장점이 있지만, 많은 양의 분광정보를 포함하고 있기 때문에 초기 수집 자료의 용량이 너무 크고, 분석을 위한 전처리 과정이 까다롭다는 단점이 있다. 반면, 10개 이하 밴드의 분광정보를 수집하는 다분광영상은 2개 밴드를 이용해 정규식생지수(NDVI)를 즉각적으로 모니터링할 수 있고, 작물의 생육현황 등을 분석할 수 있어 농업 및 산림분야에서 널리 활용되고 있다. 초분광영상을 이용한 수심산정 연구는 최적 밴드비 탐색 기법(OBRA)을 활용해 측정수심과 상관관계가 높은 밴드비를 이용해 수심맵을 구축하는 방식이 활용되어왔다. 본 연구에서는 기존의 초분광영상을 활용한 수심산정기법을 다분광영상에 적용하여 분광밴드수가 축소된(경량화된) 자료를 활용한 수심산정 가능성을 확인하기 위해 동일한 현장에서 초분광과 다분광 두가지 영상을 촬영하였으며, 각각 수심맵을 구축해 하천분야에서 다분광영상의 활용도를 평가하였다. 또한, 기존의 OBRA의 한계를 개선하기 위해 가우시안 혼합 모델(GMM; Gaussian Mixture Model)을 활용해 영상을 군집화하여 수심산정 정확도를 개선하였다.

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Analysis of suspended sediment mixing in a river confluence using UAV-based hyperspectral imagery (드론기반 초분광 영상을 활용한 하천 합류부 부유사 혼합 분석)

  • Kwon, Siyoon;Seo, Il Won;Lyu, Siwan
    • Proceedings of the Korea Water Resources Association Conference
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    • 2022.05a
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    • pp.89-89
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    • 2022
  • 하천 합류부에 지천이 유입되는 경우 복잡한 3차원적 흐름 구조를 발생시키고 이로 인해 유사혼합 및 지형 변화가 활발히 발생하게 된다. 특히, 하천 합류부에서 부유사 거동은 하천의 세굴과퇴적, 하천 지형 변화, 하천 생태계, 하천구조물 안정성 등에 직접적으로 영향을 미치기 때문에 이에 대한 정확한 분석이 하천 관리 및 재해 예방에 필수적인 요소이다. 기존의 하천 합류부 부유사 계측 자료들은 재래식 채취 방식으로 수행되어 시공간적 해상도가 매우 낮아서 실측 자료만으로 합류부에서 부유사 혼합을 분석하기에는 한계가 존재하기에 대하천의 부유사 혼합 거동 해석에 수치모형이 주로 활용되어 왔다. 본 연구에서는 하천 합류부에서 부유사 거동을 공간적으로 정밀하게 분석하기 위해 드론 기반초분광 영상을 활용하여 하천 합류부에 최적화된 부유사 계측 방법론을 제시하였다. 현장에서 계측한 초분광 자료와 부유사 농도간의 관계를 구축하기 위하여 기계학습모형인 랜덤포레스트(Random Forest) 회귀 모형과 합류부에서 분광 특성이 다른 두 하천의 특성을 정확하게 반영하기 위한 가우시안 혼합 모형 (Gaussian Mixture Model) 기반 초분광 군집화 기법을 결합하였다. 본 연구에서 구축한 방법론을 낙동강과 황강의 합류부에 적용한 결과, 초분광 군집을 통해 두하천 흐름의 경계층을 명확히 구별하였으며, 이를 바탕으로 지류와 본류에 대해 각각 분리된 회귀 모형을 구축하여 복잡한 합류부 근역 경계층에서의 부유사 거동을 보다 정확하게 재현하였다. 또한 나아가서 재현된 고해상도의 부유사 공간분포를 바탕으로 경계층에서 강한 두 흐름이 혼합되어 발생한 와류(Wake)가 부유사 혼합에 미치는 영향을 규명하였고, 하천 합류부에서 발생하는 전단층의 수평방향 대규모 와류가 부유사 혼합 양상에 지배적 영향을 미치는 것으로 확인하였다.

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Clustering Analysis of Science and Engineering College Students' understanding on Probability and Statistics (Robust PCA를 활용한 이공계 대학생의 확률 및 통계 개념 이해도 분석)

  • Yoo, Yongseok
    • Journal of Convergence for Information Technology
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    • v.12 no.3
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    • pp.252-258
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    • 2022
  • In this study, we propose a method for analyzing students' understanding of probability and statistics in small lectures at universities. A computer-based test for probability and statistics was performed on 95 science and engineering college students. After dividing the students' responses into 7 clusters using the Robust PCA and the Gaussian mixture model, the achievement of each subject was analyzed for each cluster. High-ranking clusters generally showed high achievement on most topics except for statistical estimation, and low-achieving clusters showed strengths and weaknesses on different topics. Compared to the widely used PCA-based dimension reduction followed by clustering analysis, the proposed method showed each group's characteristics more clearly. The characteristics of each cluster can be used to develop an individualized learning strategy.

Railway Track Extraction from Mobile Laser Scanning Data (모바일 레이저 스캐닝 데이터로부터 철도 선로 추출에 관한 연구)

  • Yoonseok, Jwa;Gunho, Sohn;Jong Un, Won;Wonchoon, Lee;Nakhyeon, Song
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.33 no.2
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    • pp.111-122
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    • 2015
  • This study purposed on introducing a new automated solution for detecting railway tracks and reconstructing track models from the mobile laser scanning data. The proposed solution completes following procedures; the study initiated with detecting a potential railway region, called Region Of Interest (ROI), and approximating the orientation of railway track trajectory with the raw data. At next, the knowledge-based detection of railway tracks was performed for localizing track candidates in the first strip. In here, a strip -referring the local track search region- is generated in the orthogonal direction to the orientation of track trajectory. Lastly, an initial track model generated over the candidate points, which were detected by GMM-EM (Gaussian Mixture Model-Expectation & Maximization) -based clustering strip- wisely grows to capture all track points of interest and thus converted into geometric track model in the tracking by detection framework. Therefore, the proposed railway track tracking process includes following key features; it is able to reduce the complexity in detecting track points by using a hypothetical track model. Also, it enhances the efficiency of track modeling process by simultaneously capturing track points and modeling tracks that resulted in the minimization of data processing time and cost. The proposed method was developed using the C++ program language and was evaluated by the LiDAR data, which was acquired from MMS over an urban railway track area with a complex railway scene as well.