• Title/Summary/Keyword: spectral mixture

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Performance Comparison of GMM and HMM Approaches for Bandwidth Extension of Speech Signals (음성신호의 대역폭 확장을 위한 GMM 방법 및 HMM 방법의 성능평가)

  • Song, Geun-Bae;Kim, Austin
    • The Journal of the Acoustical Society of Korea
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    • v.27 no.3
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    • pp.119-128
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    • 2008
  • This paper analyzes the relationship between two representative statistical methods for bandwidth extension (BWE): Gaussian Mixture Model (GMM) and Hidden Markov Model (HMM) ones, and compares their performances. The HMM method is a memory-based system which was developed to take advantage of the inter-frame dependency of speech signals. Therefore, it could be expected to estimate better the transitional information of the original spectra from frame to frame. To verify it, a dynamic measure that is an approximation of the 1st-order derivative of spectral function over time was introduced in addition to a static measure. The comparison result shows that the two methods are similar in the static measure, while, in the dynamic measure, the HMM method outperforms explicitly the GMM one. Moreover, this difference increases in proportion to the number of states of HMM model. This indicates that the HMM method would be more appropriate at least for the 'blind BWE' problem. On the other hand, nevertheless, the GMM method could be treated as a preferable alternative of the HMM one in some applications where the static performance and algorithm complexity are critical.

Feature Extraction Based on Speech Attractors in the Reconstructed Phase Space for Automatic Speech Recognition Systems

  • Shekofteh, Yasser;Almasganj, Farshad
    • ETRI Journal
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    • v.35 no.1
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    • pp.100-108
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    • 2013
  • In this paper, a feature extraction (FE) method is proposed that is comparable to the traditional FE methods used in automatic speech recognition systems. Unlike the conventional spectral-based FE methods, the proposed method evaluates the similarities between an embedded speech signal and a set of predefined speech attractor models in the reconstructed phase space (RPS) domain. In the first step, a set of Gaussian mixture models is trained to represent the speech attractors in the RPS. Next, for a new input speech frame, a posterior-probability-based feature vector is evaluated, which represents the similarity between the embedded frame and the learned speech attractors. We conduct experiments for a speech recognition task utilizing a toolkit based on hidden Markov models, over FARSDAT, a well-known Persian speech corpus. Through the proposed FE method, we gain 3.11% absolute phoneme error rate improvement in comparison to the baseline system, which exploits the mel-frequency cepstral coefficient FE method.

AN ADAPTED METHOD FOR REDUCING CHANGE DETECTION ERRORS DUE TO POINTING DIRECTION SHIFTS OF A SATELLITE SENSOR

  • Jeong, Jong-Hyeok;Takagi, Masataka
    • Proceedings of the KSRS Conference
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    • 2005.10a
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    • pp.126-129
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    • 2005
  • Change detections is carried out under the assumption that pixel boundaries of geometrically corrected time series satellite images cover the same location. However that assumption can be wrong when shifts in the pointing direction of a satellite sensor occurs. Currently, although the influence of misregistration on landcover change detection has been investigated, there has been little research on the influence of pointing direction shifts of a satellite sensor. In this study, a simple method for reducing the effects of pointing direction shifts of a satellite sensor is proposed: the classification of two ASTER images was carried out using the linear spectral mixture analysis, the two classification results were resampled into a geometrically fixed grid, and then the change detection of the two ASTER images was carried out by comparing the resampled classification results of the two images. The proposed method showed high performance in discriminating between changed areas and unchanged areas by removing the pointing direction shifts of a satellite sensor.

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Selective Response of Dye Rotaxane to Metal Ions (금속 이온에 대한 염료 로택산의 선택적 반응성)

  • Park, Jong-S.
    • Proceedings of the Korean Society of Dyers and Finishers Conference
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    • 2011.03a
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    • pp.35-35
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    • 2011
  • The design and preparation of novel dye rotaxanes have gained much interest recently, since such structure usually exhibits peculiar spectral and optical changes. In spite of the promising results to date, increasing pressure remains to develop novel supramolecular structures based on stimuli-responsive systems. This presentation covers the study of inclusion complexes of cyclodextrins and various chromophores, with an emphasis on our most recent outcome of anisotropic hydrogel. In this system, physical gelation prepared from simple mixture of CD and a azo dye is completed through specific host-guest interaction. The obtained hydrogel exhibits respective morphological transitions based on supramolecular assembly and dissociation, leading to either precipitation or a sol-to-gel transition. It can identify different classes of metal ions, and, among them, naked-eye differentiation of lead ion is possible due to the coordination-induced unthreading of dye molecules. Accompanying structural changes were verified by numerous characterization techniques, including 2D-ROESY, HR-MAS, UV-Visible absorption, small-angle X-ray scattering, and induced circular dichroism measurements. Such properties discussed here will find useful in analytical applications, such as metal ion sensing and removal applications.

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Phytochemical Constituents of Urtica angustifolia Fisch

  • Kwon, Hak-Cheol;Kwak, Jong-Hwan;Lee, Kang-Ro;Zee, Ok-Pyo;Yu, Seung-Jo
    • Proceedings of the Korean Society of Applied Pharmacology
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    • 1996.04a
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    • pp.168-168
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    • 1996
  • 가는잎쇄기풀(Urtica angustifolia Fisch.)은 쇄기풀과(Urticaceae)에 속하는 다년생 초본으로 중약 또는 민간에서 동속 근연식물과 함께 전초를 담마라하여 류마치스성 동통, 산후의 산풍, 소아의 추풍, 경풍,담마진의 치료에 사용되고 있다. Rat에서 실험적 항당뇨 효과를 검토해본 결과 혈당강하 작용이 있는 본 식물로부터 그 혈당강하 성분의 분리에 앞서 식물화학 성분을 규명하고자 본 실험에 착수하였다. 가는잎쇄기풀 전초의 MeOH ex.를 CH$_2$Cl$_2$, EtOAc, n-BuOT 및 $H_2O$로 분획하고 각종 column chromatography를 통하여 다수의 화합물을 분리하였다. 각 화합물은 이화학적 성상 및 spectral data로부터 scopoletin, esculetin dimethyl ether(scoparone), sterol mixture, $\beta$-sitosteryl-3-o-glucoside, kaempferol-3-o-glucoside, quercetin-3-o-glucoside, kaempferol-3-o-rutinoside로 확인하였으며 그 외 다수의 화합물은 그 구조를 규명중이다.

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DUST SHELL MODELS FOR THE YOUNG STELLAR OBJECTS IN GIANT MOLECULAR CLOUDS

  • Song, In-Ok;Suh, Kyung-Won
    • Journal of Astronomy and Space Sciences
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    • v.17 no.1
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    • pp.11-18
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    • 2000
  • We have modeled the observed spectral energy distributions(SEDs) of young stellar objects (YSOs) in giant molecular clouds (GMCs). We propose the theoretical modles for the dust envelopes around YSOs. The YSOs in a GMC may share the same initial chemical composition. In this paper, we compare the model SEDs with the observations of the YSOs. Dust shells of the YSOs are composed of a mixture of astronomical silicate and graphite grains. We propose the models for the evolution of the GMCs comparing the shape of the SEDs on the IRAS 2-color diagram with the age.

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Spectral Mixture Analysis using Hyperspectral Image for Hydrological Land Cover/Use Classification (수문학적 토지피복/이용 분류를 위한 초분광영상의 분광혼합분석)

  • Shin Jung-Il;Lee Kyu-Sung
    • Proceedings of the KSRS Conference
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    • 2006.03a
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    • pp.206-209
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    • 2006
  • 강우-유출 모델링에 있어 토지피복/이용 상태는 중요한 입력변수로 사용되지만 기존의 다중분광영상을 이용한 분류에는 한계가 있다. 본 연구에서는 위성탑재 초분광영상인 Hyperion 영상의 분광혼합분석을 통해 도시지역의 수문학적 토지피복/이용 분류를 실시하였으며 분류등급의 기준은 널리 사용되고 있는 SCS 토지피복/이용 등급을 이용하였다. 정확도분석을 위해 항공사진을 디지타이징하여 불투수면적의 비율을 비교하였으며 분광혼합분석 결과와 항공사진에서 불투수면적의 비율은 유사하게 나타났다. 그러나 SCS의 분류등급은 미국을 기준으로 개발되었기 때문에 임계치를 이용하여 분류된 등급과 실제 항공사진판독의 결과가 일부 다르게 나타나는 것을 알 수 있었다.

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Robust Non-negative Matrix Factorization with β-Divergence for Speech Separation

  • Li, Yinan;Zhang, Xiongwei;Sun, Meng
    • ETRI Journal
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    • v.39 no.1
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    • pp.21-29
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    • 2017
  • This paper addresses the problem of unsupervised speech separation based on robust non-negative matrix factorization (RNMF) with ${\beta}$-divergence, when neither speech nor noise training data is available beforehand. We propose a robust version of non-negative matrix factorization, inspired by the recently developed sparse and low-rank decomposition, in which the data matrix is decomposed into the sum of a low-rank matrix and a sparse matrix. Efficient multiplicative update rules to minimize the ${\beta}$-divergence-based cost function are derived. A convolutional extension of the proposed algorithm is also proposed, which considers the time dependency of the non-negative noise bases. Experimental speech separation results show that the proposed convolutional RNMF successfully separates the repeating time-varying spectral structures from the magnitude spectrum of the mixture, and does so without any prior training.

Novel optical properties of amorphous ferric hydroxide in near infrared region

  • Kang, Tae Yeon;Chae, Weon-Sik
    • Rapid Communication in Photoscience
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    • v.4 no.2
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    • pp.34-36
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    • 2015
  • New spectroscopic characteristics of amorphous ferric hydroxide ($Fe(OH)_3$), interestingly in the near infrared (NIR) region, are presented in this study. The absorption spectrum of ferric hydroxide covers wide spectral regions from ultraviolet to NIR (200~900 nm). Unique emission bands were newly observed in the NIR regions (800~1400 nm). Several bands of this NIR emission are quiet well overlapped with the combinational vibrational absorption bands of water. From photothermal conversion study, very interestingly, temperature of aqueous mixture solution including the amorphous ferric hydroxide was significantly increased from ambient temperature to $38^{\circ}C$ for 30 minutes under irradiation of a standard helium lamp.

GMM Based Voice Conversion Using Kernel PCA (Kernel PCA를 이용한 GMM 기반의 음성변환)

  • Han, Joon-Hee;Bae, Jae-Hyun;Oh, Yung-Hwan
    • MALSORI
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    • no.67
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    • pp.167-180
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    • 2008
  • This paper describes a novel spectral envelope conversion method based on Gaussian mixture model (GMM). The core of this paper is rearranging source feature vectors in input space to the transformed feature vectors in feature space for the better modeling of GMM of source and target features. The quality of statistical modeling is dependent on the distribution and the dimension of data. The proposed method transforms both of the distribution and dimension of data and gives us the chance to model the same data with different configuration. Because the converted feature vectors should be on the input space, only source feature vectors are rearranged in the feature space and target feature vectors remain unchanged for the joint pdf of source and target features using KPCA. The experimental result shows that the proposed method outperforms the conventional GMM-based conversion method in various training environment.

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