• The Journal of the Acoustical Society of Korea
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• v.31 no.8
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• pp.487-506
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• 2012

This paper proposes a speech enhancement algorithm to improve the speech intelligibility by suppressing both reverberation and background noise. The algorithm adopts a non-causal single-channel minimum variance distortionless response (MVDR) filter to exploit an additional information that is included in the noisy-reverberant signals in subsequent frames. The noisy-reverberant signals are decomposed into the parts of the desired signal and the interference that is not correlated to the desired signal. Then, the filter equation is derived based on the MVDR criterion to minimize the residual interference without bringing speech distortion. The estimation of the correlation parameter, which plays an important role to determine the overall performance of the system, is mathematically derived based on the general statistical reverberation model. Furthermore, the practical implementation methods to estimate sub-parameters required to estimate the correlation parameter are developed. The efficiency of the proposed enhancement algorithm is verified by performance evaluation. From the results, the proposed algorithm achieves significant performance improvement in all studied conditions and shows the superiority especially for the severely noisy and strongly reverberant environment.

• The Journal of the Acoustical Society of Korea
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• v.30 no.6
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• pp.353-359
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• 2011

This paper proposes a vocal enhancement technique for improving the performance of vocal pitch detection in polyphonic music signal. The proposed vocal enhancement technique predicts an accompaniment signal from the input signal and generates an accompaniment replica signal according to the vocal power. Then, it removes the accompaniment replica signal from the input signal, resulting in a vocal-enhanced signal. The performance of the proposed method was measured by applying the same vocal pitch extraction method to the original and the vocal-enhanced signal, and the vocal pitch detection accuracy was increased by 7.1 % point in average.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.10 no.6
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• pp.959-967
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• 1999

In this paper, we propose a Linear Prediction Method(LPM) in conjunction with signal enhancement for solving the direction-of-arrival estimation problem of multiple incoherent plane waves incident on a uniform linear array. The basic idea of signal enhancement is that of finding the covariance matrix of given rank that lies closest to a given estimated matrix in Frobenius norm sense. It is well known that LPM has a high-resolution performance in general applications, while it provides a lower statistical performance in lower SNR environment. To solve this problem, the LPM combined with signal enhancement approach is herein proposed. Simulation results are illustrated to demonstrate the better performance of the proposed method than conventional LPM.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.12
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• pp.1756-1760
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• 2015

This paper focuses on a priori signal to noise ratio (SNR) estimation method for the speech enhancement. There are many researches for speech enhancement with several ambient noise cancellation methods. The method based on spectral subtraction (SS) which is widely used in noise reduction has a trade-off between the performance and the distortion of the signals. So the need of adaptive method like an estimated a priori SNR being able to making a high performance and low distortion is increasing. The decision directed (DD) approach is used to determine a priori SNR in noisy speech signals. A priori SNR is estimated by using only the magnitude components and consequently follows a posteriori SNR with one frame delay. We propose a modified a priori SNR estimator and the weighted rational transfer function for speech enhancement with wind noises. The experimental result shows the performance of our proposed estimator is better Perceptual Evaluation of Speech Quality scores (PESQ, ITU-T P.862) compare to the conventional DD approach-based systems and different noise reduction methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2702-2719
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• 2013

A new optimal scheme based on curvelet transform is proposed for retinal image enhancement (RIE) using real-coded quantum genetic algorithm. Curvelet transform has better performance in representing edges than classical wavelet transform for its anisotropy and directional decomposition capabilities. For more precise reconstruction and better visualization, curvelet coefficients in corresponding subbands are modified by using a nonlinear enhancement mapping function. An automatic method is presented for selecting optimal parameter settings of the nonlinear mapping function via quantum genetic search strategy. The performance measures used in this paper provide some quantitative comparison among different RIE methods. The proposed method is tested on the DRIVE and STARE retinal databases and compared with some popular image enhancement methods. The experimental results demonstrate that proposed method can provide superior enhanced retinal image in terms of several image quantitative evaluation indexes.

• Journal of Korea Multimedia Society
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• v.15 no.10
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• pp.1205-1211
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• 2012

Recently, the evaluating method of the bone mineral density (BMD) in X-ray absorptiometry image has been studied for the early diagnosis of osteoporosis which is known as a metabolic disease. The BMD, in general, is evaluated by calculating pixel intensity in the bone segmented regions. Accurate bone region extraction is extremely crucial for the BMD evaluation. So, a X-Ray image enhancement is needed to get precise bone segmentation. In this paper, we propose an image enhancement method of X-ray image having multiple noise based sparse representation. To evaluate the performance of proposed method, we employ the contrast to noise ratio (CNR) metric and cut-view graphs visualizing image enhancement performance. Experimental results show that the proposed method outperforms the BayesShrink noise reduction methods and the previous noise reduction method in sparse representation with general noise model.

• The Journal of the Acoustical Society of Korea
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• v.27 no.2
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• pp.75-79
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• 2008

In this paper, we propose a novel speech enhancement technique using global soft decision (GSD) based on the probabilistic outputs of support vector machine (SVM). Generally, speech enhancement algorithms applied soft decision gain modification and noise power estimation have bettor performance than those employing hard decision. Especially, global speech absence probability (GSAP), which is known as an effective measure of the speech absence in each frame, has been adopted to SD-based speech enhancement methods. For this reason, we introduce a new GSAP estimated from the probabilistic output of SVM using sigmoid function. The performance of the proposed algorithm is evaluated by the PESQ and MOS test under various noise environments and yields better results compared with the conventional GSD scheme.

• ETRI Journal
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• v.39 no.1
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• pp.76-86
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• 2017

In this study, we propose an automatic contrast enhancement method based on transfer function modification (TFM) by histogram equalization. Previous histogram-based global contrast enhancement techniques employ histogram modification, whereas we propose a direct TFM technique that considers the mean brightness of an image during contrast enhancement. The mean point shifting method using a transfer function is proposed to preserve the mean brightness of an image. In addition, the linearization of transfer function technique, which has a histogram flattening effect, is designed to reduce visual artifacts. An attenuation factor is automatically determined using the maximum value of the probability density function in an image to control its rate of contrast. A new quantitative measurement method called sparsity of a histogram is proposed to obtain a better objective comparison relative to previous global contrast enhancement methods. According to our experimental results, we demonstrated the performance of our proposed method based on generalized measures and the newly proposed measurement.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.1
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• pp.35-44
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• 2015

The main purpose of image enhancement is to improve certain characteristics of an image to improve its visual quality. This paper proposes a method for image contrast enhancement that can be applied to both medical and natural images. The proposed algorithm is designed to achieve contrast enhancement while also preserving the local image details. To achieve this, the proposed method combines local image contrast preserving dynamic range compression and contrast limited adaptive histogram equalization (CLAHE). Global gain parameters for contrast enhancement are inadequate for preserving local image details. Therefore, in the proposed method, in order to preserve local image details, local contrast enhancement at any pixel position is performed based on the corresponding local gain parameter, which is calculated according to the current pixel neighborhood edge density. Different image quality measures are used for evaluating the performance of the proposed method. Experimental results show that the proposed method provides more information about the image details, which can help facilitate further image analysis.

• Journal of the Korea Society for Simulation
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• v.3 no.2
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• pp.49-56
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• 1994

Performance measurement and analysis for computer systems has been studied for a long time. However, little attention has been focused on how to distribute a given budget to each part of a computer system for enhancing system performance maximally. In traditional approaches, performance enhancement is achieved by identifying a dominant system bottleneck and enhancing the bottleneck's performance. These approaches, however, often bring problems since the removal of a bottleneck may result in other bottlenecks. This paper proposes an approximated method for such budget distribution problem. For budget distribution, a critical set is defined. The set contains the servers of which performance changes affect the overall system performance significantly. Then, the given budget is distributed properly to every server in the critical set. To verify the proposed method, two benchmark experiments are carried out I SLAM environments. The experimental results show that the proposed method provides better results than the traditional method does in many cases.