• Title, Summary, Keyword: Gaussian mixture model

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Feature Selection for Multi-Class Genre Classification using Gaussian Mixture Model (Gaussian Mixture Model을 이용한 다중 범주 분류를 위한 특징벡터 선택 알고리즘)

  • Moon, Sun-Kuk;Choi, Tack-Sung;Park, Young-Cheol;Youn, Dae-Hee
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.32 no.10C
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    • pp.965-974
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    • 2007
  • In this paper, we proposed the feature selection algorithm for multi-class genre classification. In our proposed algorithm, we developed GMM separation score based on Gaussian mixture model for measuring separability between two genres. Additionally, we improved feature subset selection algorithm based on sequential forward selection for multi-class genre classification. Instead of setting criterion as entire genre separability measures, we set criterion as worst genre separability measure for each sequential selection step. In order to assess the performance proposed algorithm, we extracted various features which represent characteristics such as timbre, rhythm, pitch and so on. Then, we investigate classification performance by GMM classifier and k-NN classifier for selected features using conventional algorithm and proposed algorithm. Proposed algorithm showed improved performance in classification accuracy up to 10 percent for classification experiments of low dimension feature vector especially.

Text-Independent Speaker Verification Using Variational Gaussian Mixture Model

  • Moattar, Mohammad Hossein;Homayounpour, Mohammad Mehdi
    • ETRI Journal
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    • v.33 no.6
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    • pp.914-923
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    • 2011
  • This paper concerns robust and reliable speaker model training for text-independent speaker verification. The baseline speaker modeling approach is the Gaussian mixture model (GMM). In text-independent speaker verification, the amount of speech data may be different for speakers. However, we still wish the modeling approach to perform equally well for all speakers. Besides, the modeling technique must be least vulnerable against unseen data. A traditional approach for GMM training is expectation maximization (EM) method, which is known for its overfitting problem and its weakness in handling insufficient training data. To tackle these problems, variational approximation is proposed. Variational approaches are known to be robust against overtraining and data insufficiency. We evaluated the proposed approach on two different databases, namely KING and TFarsdat. The experiments show that the proposed approach improves the performance on TFarsdat and KING databases by 0.56% and 4.81%, respectively. Also, the experiments show that the variationally optimized GMM is more robust against noise and the verification error rate in noisy environments for TFarsdat dataset decreases by 1.52%.

Text Segmentation from Images with Various Light Conditions Based on Gaussian Mixture Model

  • Tran, Khoa Anh;Lee, Gueesang
    • International Journal of Contents
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    • v.9 no.1
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    • pp.1-5
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    • 2013
  • Standard Gaussian Mixture Model (GMM) is a well-known method for image segmentation. However, one of its problems is that we consider the pixel as independent to each other, which can cause the segmentation results sensitive to noise. It explains why some of existing algorithms still cannot segment texts from the background clearly. Therefore, we present a new method in which we incorporate the spatial relationship between a pixel and its neighbors inside $3{\times}3$ windows to segment the text. Our approach works well with images containing texts, which has different sizes, shapes or colors in case of light changes or complex background. Experimental results demonstrate the robustness, accuracy and effectiveness of the proposed model in image segmentation compared to other methods.

An Intelligent Automatic Early Detection System of Forest Fire Smoke Signatures using Gaussian Mixture Model

  • Yoon, Seok-Hwan;Min, Joonyoung
    • Journal of Information Processing Systems
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    • v.9 no.4
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    • pp.621-632
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    • 2013
  • The most important things for a forest fire detection system are the exact extraction of the smoke from image and being able to clearly distinguish the smoke from those with similar qualities, such as clouds and fog. This research presents an intelligent forest fire detection algorithm via image processing by using the Gaussian Mixture model (GMM), which can be applied to detect smoke at the earliest time possible in a forest. GMMs are usually addressed by making the model adaptive so that its parameters can track changing illuminations and by making the model more complex so that it can represent multimodal backgrounds more accurately for smoke plume segmentation in the forest. Also, in this paper, we suggest a way to classify the smoke plumes via a feature extraction using HSL(Hue, Saturation and Lightness or Luminanace) color space analysis.

Model-based Clustering of DOA Data Using von Mises Mixture Model for Sound Source Localization

  • Dinh, Quang Nguyen;Lee, Chang-Hoon
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.13 no.1
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    • pp.59-66
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    • 2013
  • In this paper, we propose a probabilistic framework for model-based clustering of direction of arrival (DOA) data to obtain stable sound source localization (SSL) estimates. Model-based clustering has been shown capable of handling highly overlapped and noisy datasets, such as those involved in DOA detection. Although the Gaussian mixture model is commonly used for model-based clustering, we propose use of the von Mises mixture model as more befitting circular DOA data than a Gaussian distribution. The EM framework for the von Mises mixture model in a unit hyper sphere is degenerated for the 2D case and used as such in the proposed method. We also use a histogram of the dataset to initialize the number of clusters and the initial values of parameters, thereby saving calculation time and improving the efficiency. Experiments using simulated and real-world datasets demonstrate the performance of the proposed method.

A Study on Improved MDL Technique for Optimization of Acoustic Model (향상된 MDL 기법에 의한 음향모델의 최적화 연구)

  • Cho, Hoon-Young;Kim, Sang-Hun
    • The Journal of the Acoustical Society of Korea
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    • v.29 no.1
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    • pp.56-61
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    • 2010
  • This paper describes optimization methods of acoustic models in HMM-based continuous speech recognition. Most of the conventional speech recognition systems use the same number of Gaussian mixture components for each HMM state. However, since the number of data samples available for each state is different from each other, it is possible to reduce the overall number of model parameters and the computational cost at the decoding step by optimizing the number of Gaussian mixture components. In this study, we introduced the Gaussian mixture weight term at the merging stage of Gaussian components in the minimum description length (MDL) based acoustic modeling optimization. Experimental results showed that the proposed method can obtain better ASR accuracy than the previous optimization method which does not consider the Gaussian mixture weight term.

Background Subtraction based on GMM for Night-time Video Surveillance (야간 영상 감시를 위한 GMM기반의 배경 차분)

  • Yeo, Jung Yeon;Lee, Guee Sang
    • Smart Media Journal
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    • v.4 no.3
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    • pp.50-55
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    • 2015
  • In this paper, we present background modeling method based on Gaussian mixture model to subtract background for night-time video surveillance. In night-time video, it is hard work to distinguish the object from the background because a background pixel is similar to a object pixel. To solve this problem, we change the pixel of input frame to more advantageous value to make the Gaussian mixture model using scaled histogram stretching in preprocessing step. Using scaled pixel value of input frame, we then exploit GMM to find the ideal background pixelwisely. In case that the pixel of next frame is not included in any Gaussian, the matching test in old GMM method ignores the information of stored background by eliminating the Gaussian distribution with low weight. Therefore we consider the stacked data by applying the difference between the old mean and new pixel intensity to new mean instead of removing the Gaussian with low weight. Some experiments demonstrate that the proposed background modeling method shows the superiority of our algorithm effectively.

Improved Algorithm for Fully-automated Neural Spike Sorting based on Projection Pursuit and Gaussian Mixture Model

  • Kim, Kyung-Hwan
    • International Journal of Control, Automation, and Systems
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    • v.4 no.6
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    • pp.705-713
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    • 2006
  • For the analysis of multiunit extracellular neural signals as multiple spike trains, neural spike sorting is essential. Existing algorithms for the spike sorting have been unsatisfactory when the signal-to-noise ratio(SNR) is low, especially for implementation of fully-automated systems. We present a novel method that shows satisfactory performance even under low SNR, and compare its performance with a recent method based on principal component analysis(PCA) and fuzzy c-means(FCM) clustering algorithm. Our system consists of a spike detector that shows high performance under low SNR, a feature extractor that utilizes projection pursuit based on negentropy maximization, and an unsupervised classifier based on Gaussian mixture model. It is shown that the proposed feature extractor gives better performance compared to the PCA, and the proposed combination of spike detector, feature extraction, and unsupervised classification yields much better performance than the PCA-FCM, in that the realization of fully-automated unsupervised spike sorting becomes more feasible.

Sound Reinforcement Based on Context Awareness for Hearing Impaired (청각장애인을 위한 상황인지기반의 음향강화기술)

  • Choi, Jae-Hun;Chang, Joon-Hyuk
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.48 no.5
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    • pp.109-114
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    • 2011
  • In this paper, we apply a context awareness based on Gaussian mixture model (GMM) to a sound reinforcement for hearing impaired. In our approach, the harmful sound amplified through the sound reinforcement algorithm according to context awareness based on GMM which is constructed as Mel-frequency cepstral coefficients (MFCC) feature vector from sound data. According to the experimental results, the proposed approach is found to be effective in the various acoustic environments.

Object Detection by Gaussian Mixture Model and Shape Adaptive Bidirectional Block Matching Algorithm

  • Park, Goo-Man;Han, Byung-Wan;An, Tae-Ki;Lee, Kwang-Jeek
    • Journal of Broadcast Engineering
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    • v.13 no.5
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    • pp.681-684
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    • 2008
  • We proposed a method to improve moving object detection capability of Gaussian Mixture Model by suggesting shape adaptive bidirectional block matching algorithm. This method achieves more accurate detection and tracking performance at various motion types such as slow, fast, and bimodal motions than that of Gaussian Mixture Model. Experimental results showed that the proposed method outperformed the conventional methods.