• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제11권4호
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• pp.47-54
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• 2007

In [6] some computation of spectral measures induced by normal operators $T^{*n}T^n$ was introduced. In this note we improve some computations by using spectral measures, which are related t o extremal vectors. Also, we discuss the extremal value properties and apply our spectral measure equations to moment sequences which are induced by weighted shifts.

• 한국정보통신학회논문지
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• 제5권3호
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• pp.443-449
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• 2001

잡음환경에서의 음성인식은 실제의 환경에서의 음성인식에서 매우 중요한 애로기술로써 이를 해결하기 위한 연구는 꾸준히 연구되고 있다. 따라서 본 연구는 음성인식분야에서 가장 많이 사용하고 있는 HMM처리 시잡음처리의 문제점을 주파수 가중치 부가 HMM으로 해결하는 방법을 제안하고 그 성능을 인식실험을 통하여 검토하였다. 그 결과 SS처리를 함께 사용하는 $MCE-\mu$, MCE-$\rho$가 가장 잡음에 강한 방식임을 알 수 있었다.

• ETRI Journal
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• 제33권1호
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• pp.99-109
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• 2011

This paper concerns a robust real-time voice activity detection (VAD) approach which is easy to understand and implement. The proposed approach employs several short-term speech/nonspeech discriminating features in a voting paradigm to achieve a reliable performance in different environments. This paper mainly focuses on the performance improvement of a recently proposed approach which uses spectral peak valley difference (SPVD) as a feature for silence detection. The main issue of this paper is to apply a set of features with SPVD to improve the VAD robustness. The proposed approach uses a weighted voting scheme in order to take the discriminative power of the employed feature set into account. The experiments show that the proposed approach is more robust than the baseline approach from different points of view, including channel distortion and threshold selection. The proposed approach is also compared with some other VAD techniques for better confirmation of its achievements. Using the proposed weighted voting approach, the average VAD performance is increased to 89.29% for 5 different noise types and 8 SNR levels. The resulting performance is 13.79% higher than the approach based only on SPVD and even 2.25% higher than the not-weighted voting scheme.

• Current Optics and Photonics
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• 제5권4호
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• pp.431-443
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• 2021

Sparse unmixing has been proven to be an effective method for hyperspectral unmixing. Hyperspectral images contain rich spectral and spatial information. The means to make full use of spectral information, spatial information, and enhanced sparsity constraints are the main research directions to improve the accuracy of sparse unmixing. However, many algorithms only focus on one or two of these factors, because it is difficult to construct an unmixing model that considers all three factors. To address this issue, a novel algorithm called multiview-based spectral weighted and low-rank row-sparsity unmixing is proposed. A multiview data set is generated through spectral partitioning, and then spectral weighting is imposed on it to exploit the abundant spectral information. The row-sparsity approach, which controls the sparsity by the l_2,0 norm, outperforms the single-sparsity approach in many scenarios. Many algorithms use convex relaxation methods to solve the l_2,0 norm to avoid the NP-hard problem, but this will reduce sparsity and unmixing accuracy. In this paper, a row-hard-threshold function is introduced to solve the l_2,0 norm directly, which guarantees the sparsity of the results. The high spatial correlation of hyperspectral images is associated with low column rank; therefore, the low-rank constraint is adopted to utilize spatial information. Experiments with simulated and real data prove that the proposed algorithm can obtain better unmixing results.

• 한국음향학회:학술대회논문집
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• 한국음향학회 1985년도 학술발표회 논문집
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• pp.87-94
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• 1985

In this paper, enhancement of speech corrupted by additive white or colored noise is stuided. The nuconstrained frequency-domain block least-mean-square (UFBLMS) adaptation algorithm and its frequency-weighted version are newly applied to speech enhancement. For enhancement of speech degraded by white noise, the performance of the UFBLMS algorithm is superior to the spectral subtraction method or Wiener filtering technique by more than 3 dB in segmented frequency-weighted signal-to-noise ratio(FWSNERSEG) when SNR of speech is in the range of 0 to 10 dB. As for enhancement of noisy speech corrupted by colored noise, the UFBLMS algorithm is superior to that of the spectral subtraction method by about 3 to 5 dB in FWSNRSEG. Also, it yields better performance by about 2 dB in FWSNR and FWSNRSEG than that of time-domain least-mean-square (TLMS) adaptive prediction filter(APF). In view of the computational complexity and performance improvement in speech quality and intelligibility, the frequency-weighted UFBLMS algorithm appears to yield the best performance among various algorithms in enhancing noisy speech corrupted by white or colored noise.

• 한국멀티미디어학회논문지
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• 제7권12호
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• pp.1639-1649
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• 2004

In this paper, a method for detection of forged signatures based on spectral analysis of directional gradient density function and a weighted fuzzy classifier is proposed. The well defined outline of an incoming signature image is extracted in a preprocessing stage which includes noise reduction, automatic thresholding, image restoration and erosion process. The directional gradient density function derived from extracted signature outline is highly related to the overall shape of signature image, and thus its frequency spectrum is used as a feature set. With this spectral feature set, having a property to be invariant in size, shift, and rotation, a weighted fuzzy classifier is evaluated for the verification of freehand and random forgeries. Experiments show that less than 5% averaged error rate can be achieved on a database of 500 signature samples.

• 대한수학회보
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• 제53권1호
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• pp.247-262
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• 2016

We propose and analyze spectral and pseudo-spectral Jacobi-Galerkin approaches for weakly singular Volterra integral equations (VIEs). We provide a rigorous error analysis for spectral and pseudo-spectral Jacobi-Galerkin methods, which show that the errors of the approximate solution decay exponentially in $L^{\infty}$ norm and weighted $L^2$-norm. The numerical examples are given to illustrate the theoretical results.

• 한국음향학회지
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• 제21권2호
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• pp.167-172
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• 2002

음성부호화 알고리즘인 EVRC (Enhanced Variable Rate Codec)는 현재 북미 및 한국 CDMA 디지털 셀룰러 시스템에 사용되고 있다. EVRC음성부호화기에서 음성의 주파수영역에서의 에너지 분포와 관련되어 있는 LSP (Line Spectral Pairs)값은 가중분할 벡터 양자화 (Weighted Split Vector Quantization)에 의해 코딩된다. 이러한 코딩 과정에 사용되는 LSP 코드북이 개발국 언어 혹은 영어로 설계되었음을 감안하면 한국어통화에 대해서는 한국어로 설계된 LS 코드북에 의해 향상된 성능을 기대할 수 있다. 본 논문에서는 한국어로 BVRC의 LSP 코드북을 LBG알고리즘을 기반으로 한 벡터 양자화기법으로 설계하였으며 이 코드북에 의한 벡터양자화 성능향상 및 그에 따른 음질향상을 각각 SD (Spectral Distortion) 및 신호대 잡음비 (SNR), SegSNR측정으로 입증하였다.

• 대한기계학회논문집B
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• 제23권5호
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• pp.628-636
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• 1999

This work describes the low-resolution spectral modeling of the water vapor, carbon dioxide and their mixtures by applying the weighted-sum-of-gray-gas-gases model (WSGGM) to each narrow band. Proper modeling scheme of gray gas absorption coefficients vs temperature relation is suggested. Comparison between the modeled emissivity calculated from this relation and the 'true' emissivity obtained from the high temperature statistical narrow band parameters is made for a few typical narrow bands. Low resolution spectral intensities from one-dimensional layers are also obtained and examined for uniform, parabolic and boundary layer type temperature profiles using the obtained WSGGM's with several gray gases. The results are compared with the narrow band spectral intensities obtained by a narrow band model-based code with Curtis-Godson approximation. Good agreement is found between them. Data bases including optimized modeling parameters and total and low-resolution spectral weighting factors are developed for water vapor, carbon dioxide and their mixtures. This model and obtained data bases, available from the authors' Internet site, can be appropriately applied to any radiative transfer equation solver.

• 한국정보통신학회논문지
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• 제14권4호
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• pp.959-966
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• 2010

정량적 초음파 분석(Quantitative Ultrasound Analysis)은 반향된 초음파 신호의 짧은 시간 간격의 주파수 성분을 추출하여 개별 초음파 지수의 값을 예측한다. 따라서 반향 신호의 정확한 주파수 특성 추출은 분석의 정확도와 정밀도 향상에 기본이 된다. 본 논문에서는 초음파 지수의 정량적인 예측 및 분석에 이용할 수 있는, 짧은 시간 간격의 반향 신호의 주파수 특성 추출 방법을 제안한다. 제안된 알고리듬은 인접한 반향 초음파 신호간의 위상 차이를 보상하고, 동일 반향 깊이를 가지는 작은 영역의 신호를 가중치 평균함으로써 보다 정확한 주파수 특성을 추출한다. 컴퓨터 모의 실험을 통한 수치 분석 결과, 제안된 알고리듬은 일반적인 주파수 추출 알고리듬보다 정확한 예측 결과를 보였으며, 예측 결과의 정밀도도 10% 이상 향상되었다.