• 대한음성학회지:말소리
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• 제61호
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• pp.63-74
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• 2007

Discrimination of music and speech from the multimedia signal is an important task in audio coding and broadcast monitoring systems. This paper deals with the problem of feature parameter extraction for discrimination of music and speech. The wavelet transform is a multi-resolution analysis method that is useful for analysis of temporal and spectral properties of non-stationary signals such as speech and audio signals. We propose new feature parameters extracted from the wavelet transformed signal for discrimination of music and speech. First, wavelet coefficients are obtained on the frame-by-frame basis. The analysis frame size is set to 20 ms. A parameter $E_{sum}$ is then defined by adding the difference of magnitude between adjacent wavelet coefficients in each scale. The maximum and minimum values of $E_{sum}$ for period of 2 seconds, which corresponds to the discrimination duration, are used as feature parameters for discrimination of music and speech. To evaluate the performance of the proposed feature parameters for music and speech discrimination, the accuracy of music and speech discrimination is measured for various types of music and speech signals. In the experiment every 2-second data is discriminated as music or speech, and about 93% of music and speech segments have been successfully detected.

• Journal of Information Processing Systems
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• 제13권5호
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• pp.1183-1202
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• 2017

Image restoration has been carried out by texture synthesis mostly for large regions and inpainting algorithms for small cracks in images. In this paper, we propose a new approach that allows for the simultaneous fill-in of different structures and textures by processing in a wavelet domain. A combination of structure inpainting and patch-based texture synthesis is carried out, which is known as patch-based inpainting, for filling and updating the target region. The wavelet transform is used for its very good multiresolution capabilities. The proposed algorithm uses the wavelet domain subbands to resolve the structure and texture components in smooth approximation and high frequency structural details. The subbands are processed separately by the prioritized patch-based inpainting with isophote energy driven texture synthesis at the core. The algorithm automatically estimates the wavelet coefficients of the target regions of various subbands using optimized patches from the surrounding DWT coefficients. The suggested performance improvement drastically improves execution speed over the existing algorithm. The proposed patch optimization strategy improves the quality of the fill. The fill-in is done with higher priority to structures and isophotes arriving at target boundaries. The effectiveness of the algorithm is demonstrated with natural and textured images with varying textural complexions.

• 대한의용생체공학회:의공학회지
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• 제24권2호
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• pp.99-106
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• 2003

본 논문에서는 정신질환 진단을 위하여 뇌파신호를 판별하는 방법을 제안하였다. 정신질환의 한 종류인 정신분열증 환자의 뇌파와 정상인의 뇌파를 분류하기 위하여 제안한 방법에서는 기본적으로 웨이블릿 변환과 인공 신경망을 이용하였다. 뇌파 신호에 웨이블릿 변환을 적용하여 각각 알파. 베타. 세타 그리고 델타파에 해당하는 주파수 대역의 웨이블릿 계수를 구한 다음. 각각의 주파수 대역에 대한 웨이블릿 계수들의 크기 평균 및 분산들을 인공 신경망의 입력 데이터로 이용하였다. 인공 신경망은 2개의 은닉층을 갖는 4층의 피드포워드 회로망 구조를 가지며 학습에는 역전파 학습 알고리듬을 이용하였다. 정신분열증의 판별시스템은 19 채널의 뇌파신호에 대응하는 19개의 인공신경망으로 구성되었고 정상인과 정신분열증 환자에 대하여 각각 100%와 86.67%의 정확도를 보여주었다.

• 한국정보전자통신기술학회논문지
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• 제2권4호
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• pp.19-28
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• 2009

본 연구에서는 웨이블렛 변환에 기반한 EZW알고리즘을 적용하여 효율성 있는 영상 압축방법을 제시하였다. 이 방법은 영상을 Daubechies 필터에 의하여 웨이블렛 변환하고 대역간 웨이블렛 계수들의 상관관계를 이용하여 제로트리 부호화하는 EZW 알고리즘을 적용하여 웨이블렛 계수의 크기가 임계값이상이면 POS, NEG 심볼로 분류하고 임계값 이하이면 IZ, ZTR 심볼로 분류하여 주 부호화과정과 종속 부호화과정을 수행하며 임계값을 반으로 줄이면서 부호화과정을 반복하는 영상압축방법이다. 본 연구에서 제시한 방법을 수행하여 영상압축한 결과를 정지영상압축방법에 사용되는 JPEG알고리즘에 의하여 수행한 영상압축결과와 비교한 결과 본 연구에서 제시한 영상압축결과가 부호화 복호화에서 원하는 비트율로 조절할 수 있고 낮은 비트에서 우수한 PSNR값을 나타내며 블록현상이 발생하지 않아 JPEG의 영상압축방법보다 우수하다는 것을 확인하였다.

• 인터넷정보학회논문지
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• 제8권1호
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• pp.125-132
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• 2007

본 논문은 가중 퍼지소속함수 기반 신경망(neural network with weighted fuzzy membership functions, NEWFM)을 이용하여 심전도(ECG) 신호로부터 조기심실수축(premature vedtricular contractions, PVC)을 자동 탐지하는 방안을 제시하고 있다. NEWFM은 MIT-BIH 데이터베이스의 부정맥 심전도를 웨이블릿 변환(wavelet transform, WT)한 계수로부터 학습하여 정상 파형과 PVC 파형을 구분한다. 비중복면적 분산 측정법을 적용하여 중요도가 가장 높은 웨이블릿 변환의 d3과 d4의 8개 계수를 추출하였다. 이들 특징입력을 3개의 실험군에 사용하여 각각 99.80%, 99.21%, 98.78%의 신뢰성 있는 전체분류율을 나타내었고, 이는 각 실험군에 대한 특징입력의 종속성이 적음을 보여준다. 추출된 8개 계수의 ECG 신호 구간과 퍼지소속함수를 제시함으로써 특징입력에 대한 명시적인 해석을 가능하게 하였다.

• Journal of applied mathematics & informatics
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• 제29권3_4호
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• pp.1037-1042
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• 2011

Multiwavelet coefficients can be constructed from the multi-scaling coefficients by using the factorization for paraunitary matrices. In this paper we present a procedure for parametrizing all possible multi-wavelet coefficients corresponding to the multiscaling coefficients of DGHM type.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2005년도 추계종합학술대회
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• pp.343-346
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• 2005

In this study, a basic concept of wavelet encoding and its advantages over Fourier based phase encoding application. Wavelet encoding has been proposed as an alternative way to Fourier based phase encoding in magnetic resonance imaging. In wavelet encoding, the RF pulse is designed to generate wavelet-shaped excitation profile of spins. From the resulting echo signals, the wavelet transform coefficients of spin distribution are acquired and an original spin density is reconstructed from wavelet expansion. Wavelet encoding has several advantages over phase encoding. By minimizing redundancy of the data acquisition in a dynamic series of images, we can avoid some encoding steps without serious loss of quality in reconstructed image. This strategy may be regarded as data compression during imaging. Although there are some limitations in wavelet encoding, it is a promising scheme in a dynamic imaging.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 G
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• pp.2956-2958
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• 1999

The classical solution to the noise removal problem is the Wiener filter, which utilizes the second-order statistics of the Fourier decomposition. We discuss a Bayesian formalism which gives rise to a type of wavelet threshold estimation in non-parametric regression. A prior distribution is imposed on the wavelet coefficients of the unknown response function, designed to capture the sparseness of wavelet expansion common to most application. For the prior specified, the posterior median yields a thresholding procedure

• 한국시뮬레이션학회논문지
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• 제10권3호
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• pp.95-103
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• 2001

Rendering of 3D terrain data in real-time is difficult because of its large scale. So, it is necessary to use level-of-detail(LOD) that uses fewer data, but makes almost similar image to the original. We present an algorithm for real-time LOD generation and rendering of 3D terrain data. The algorithm applies wavelet transform to the terrain data, and then generates quadtree based view-dependent LOD using wavelet coefficients that are the output of wavelet transform. It also uses frame-to-frame coherence and view culling for high frame rates.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2008년도 International Symposium on Remote Sensing
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• pp.169-172
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• 2008

Land-use or land-cover classification of satellite images is one of the important tasks in remote sensing application and many researchers have been tried to enhance classification accuracy. Previous studies show that the classification technique based on wavelet transform is more effective than that of traditional techniques based on original pixel values, especially in complicated imagery. Various wavelets can be used in wavelet transform. Wavelets are used as basis functions in representing other functions, like sinusoidal function in Fourier analysis. In these days, some basis functions such as Haar, Daubechies, Coiflets and Symlets are mainly used in 2D image processing. Selecting adequate wavelet is very important because different results could be obtained according to the type of basis function in classification. However, it is not easy to choose the basis function which is effective to improve classification accuracy. In this study, we computed the wavelet coefficients of satellite image using 10 different basis functions, and then classified test image. After evaluating classification results, we tried to ascertain which basis function is the most effective for image classification. We also tried to see if the optimum basis function is decided by energy parameter before classifying the image using all basis function. The energy parameter of signal is the sum of the squares of wavelet coefficients. The energy parameter is calculated by sub-bands after the wavelet decomposition and the energy parameter of each sub-band can be a favorable feature of texture. The decision of optimum basis function using energy parameter in the wavelet based image classification is expected to be helpful for saving time and improving classification accuracy effectively.