• Title/Summary/Keyword: Feature Set

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Automatic Speaker Identification by Sustained Vowel Phonation (지속적으로 발성한 모음에 의한 화자인식)

  • Bae, Geon-Seong
    • The Journal of the Acoustical Society of Korea
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    • v.11 no.1
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    • pp.35-41
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    • 1992
  • A speaker identification scheme using the speaker-based VQ codecook of a sustained vowel is proposed and tested. With the pitch synchronous LPC vector of the sustained vowel /i/ as a feature vector, a VQ codebook size of 4 was found to be suitable to characterize each speaker's feature space. For 40 normal speakers (20 males, 20 females), we achieved the correct identification rate of 99.4% with a training data set, and 89.4% with a test data set with speech samples of only 50 pitch periods.

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A Study on the Extraction of Knowledge for Image Understanding (영상이해를 위한 지식유출에 관한 연구)

  • 곽윤식;이대영
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.18 no.5
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    • pp.757-772
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    • 1993
  • This paper describes the knowledge extraction for image understanding in knowledge based system. The current set of low level processes operate on the numerical pixel arrays, to segment the image into region and to convert the image into directional image, and to calculate feature for these regions. The current set of intermedate level processes operate on the results of earlier knowledge source to build more complex representations of the data. We have grouped into thee categories : feature based classification, geometric token relation, perceptual organization and grouping.

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Modality-Based Sentence-Final Intonation Prediction for Korean Conversational-Style Text-to-Speech Systems

  • Oh, Seung-Shin;Kim, Sang-Hun
    • ETRI Journal
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    • v.28 no.6
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    • pp.807-810
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    • 2006
  • This letter presents a prediction model for sentence-final intonations for Korean conversational-style text-to-speech systems in which we introduce the linguistic feature of 'modality' as a new parameter. Based on their function and meaning, we classify tonal forms in speech data into tone types meaningful for speech synthesis and use the result of this classification to build our prediction model using a tree structured classification algorithm. In order to show that modality is more effective for the prediction model than features such as sentence type or speech act, an experiment is performed on a test set of 970 utterances with a training set of 3,883 utterances. The results show that modality makes a higher contribution to the determination of sentence-final intonation than sentence type or speech act, and that prediction accuracy improves up to 25% when the feature of modality is introduced.

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A Feature Set Description of Grammar Rules for Natureal Languages (자질 집합 표현에 의한 자연언어 문법 규칙 기술)

  • Park, Sung-Suk;Han, Sung-Kook
    • Annual Conference on Human and Language Technology
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    • 1994.11a
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    • pp.419-425
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    • 1994
  • 문법체계내의 문법규칙은 규칙의 기술양식에 의해 언어특성이 결정된다. 본 논문에서는 문법 체계의 규칙기술을 위한 새로운 자질 집합 기술 (feature set description)을 제안하고, 이를 기반으로 한 파라메터화된 문맥자유문법 (parametrized context-free grammar : PCFG)을 정의하여, 자연언어의 문법규칙을 구성하는 방법에 대하여 기술한다. 자질 집합 기술은 간결한 규칙체계를 유지하면서 강력한 생성능력을 갖는 문법체계를 구현할 수 있어, 자연언어 처리 시스템에 효과적으로 적용할 수 있음을 보였다.

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System Architecture and Datum Reference Frame for Computer Aided Fixture Planning System (치구계획의 자동화시스템 구성 및 데이텀 체계의 결정)

  • Cho, Kyu-Kab;Jeong, Yeong-Deug
    • IE interfaces
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    • v.4 no.2
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    • pp.1-12
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    • 1991
  • This paper deals with the development of a computer aided fixture planning system that automatically selects set-ups, set-up sequence and fixture design for prismatic parts. This study presents the hierarchical data structure for feature-based part model and the preprocessing procedure for the proposed system. The preprocessing procedure generates tools such as DDR(Degree of Dimensional Relationship), AMV(Admissible Misalignment Value) and the datum reference frame of each feature according to the proposed decision table. The proposed system is called AFIX(Automated FIXture planning system) which is implemented by using C language on the workstation. A case study for a cavity plate is presented to show the performance of the AFM.

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A 3D TEXTURE SYNTHESIS APPROACH

  • Su, Ya-Lin;Chang, Chin-Chen;Shih, Zen-Chung
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • 2009.01a
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    • pp.28-31
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    • 2009
  • In this paper, a new approach for solid texture synthesis from input volume data is presented. In the pre-process, feature vectors and a similarity set were constructed for input volume data. The feature vectors were used to construct neighboring vectors for more accurate neighborhood matching. The similarity set which recorded 3 candidates for each voxel helped more effective neighborhood matching. In the synthesis process, the pyramid synthesis method was used to synthesize solid textures from coarse to fine level. The results of the proposed approach were satisfactory.

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얼굴의 geometry 특징을 이용한 다중해상도 템플릿 매칭 얼굴 특징 추출법

  • Yun, Seong-Uk;Kim, Jae-Min;Jo, Seong-Won;Choe, Gyeong-Sam;Baek, Seong-Uk
    • Proceedings of the KIEE Conference
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    • 2003.11c
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    • pp.1002-1005
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    • 2003
  • This paper presents a new template matching method for finding facial feature points. Instead of matching each template to its corresponding feature point separately the present method matches a set of element templates simultaneously. The set of templates can be placed on the space predefined by the geometrical characteristics of human faces. As a result, the search area for template matching is very small compared with a conventional method. This makes the presented method very robust and accurate. Experiment results show that the presented method results in good performance In various illuminance environments and poses.

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A New Variational Level Set Evolving Algorithm for Image Segmentation

  • Fei, Yang;Park, Jong-Won
    • Journal of Information Processing Systems
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    • v.5 no.1
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    • pp.1-4
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    • 2009
  • Level set methods are the numerical techniques for tracking interfaces and shapes. They have been successfully used in image segmentation. A new variational level set evolving algorithm without re-initialization is presented in this paper. It consists of an internal energy term that penalizes deviations of the level set function from a signed distance function, and an external energy term that drives the motion of the zero level set toward the desired image feature. This algorithm can be easily implemented using a simple finite difference scheme. Meanwhile, not only can the initial contour can be shown anywhere in the image, but the interior contours can also be automatically detected.

Numerical analysis of three-dimensional sloshing flow using least-square and level-set method (최소자승법과 Level-set 방법을 이용한 3차원 슬로싱 유동의 수치해석)

  • Choi, Hyoung-Gwon
    • Proceedings of the KSME Conference
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    • 2008.11b
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    • pp.2401-2405
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    • 2008
  • In the present study, a three-dimensional least square/level set based two-phase flow code was developed for the simulation of three-dimensional sloshing problems using finite element discretization. The present method can be utilized for the analysis of a free surface flow problem in a complex geometry due to the feature of FEM. Since the finite element method is employed for the spatial discretization of governing equations, an unstructured mesh can be naturally adopted for the level set simulation of a free surface flow without an additional load for the code development except that solution methods of the hyperbolic type redistancing and advection equations of the level set function should be devised in order to give a bounded solution on the unstructured mesh. From the numerical experiments of the present study, it is shown that the proposed method is both robust and accurate for the simulation of three-dimensional sloshing problems.

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Classification of Epilepsy Using Distance-Based Feature Selection (거리 기반의 특징 선택을 이용한 간질 분류)

  • Lee, Sang-Hong
    • Journal of Digital Convergence
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    • v.12 no.8
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    • pp.321-327
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    • 2014
  • Feature selection is the technique to improve the classification performance by using a minimal set by removing features that are not related with each other and characterized by redundancy. This study proposed new feature selection using the distance between the center of gravity of the bounded sum of weighted fuzzy membership functions (BSWFMs) provided by the neural network with weighted fuzzy membership functions (NEWFM) in order to improve the classification performance. The distance-based feature selection selects the minimum features by removing the worst features with the shortest distance between the center of gravity of BSWFMs from the 24 initial features one by one, and then 22 minimum features are selected with the highest performance result. The proposed methodology shows that sensitivity, specificity, and accuracy are 97.7%, 99.7%, and 98.7% with 22 minimum features, respectively.