• The Journal of the Acoustical Society of Korea
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• v.13 no.2
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• pp.76-85
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• 1994

A speech enhancement algorithm which attenuates nonstationary colored noise is presented In this paper. The algorithm consists of a stationary Kalman filter and the simple speech/nonspeech detector. While the conventional enhancement systems are focused on a stationary and/or white background noise, this study Is focused on the mort realistic nonstationary and nonwhite noise. An AR model-based vector Kalman filter is used as a noise suppression system and a short-time energy threshold logic is used as a speech/nonspeech classifier. For Kalman filtering. noise coefficients are estimated in the nonspeech frame, and speech coefficients are estimated by applying the EM iteration algorithm. Simulation results using the car noise are presented based on the signal-to-noise ratio and informal listening tests. According to the experimental results, background noises in the nonspeech frames are eliminated almost completely, while some distortions are noticed in the speech frames. The distortion becomes severer as the SNR is reduced to 0dB and -5dB. Intelligibility, however, is not degraded significantly.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.9
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• pp.1319-1324
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• 2013

The mean shift algorithm has achieved considerable success in object tracking due to its simplicity and robustness. It finds local minima of a similarity measure between the color histograms or kernel density estimates of the target and candidate image. However, it is sensitive to the noises due to objects or background having similar color distributions. In addition, occlusion by another object often causes a face region to change in size and position although a face region is a critical clue to perform face recognition or compute face orientation. We assume that depth and color are effective to separate a face from a background and a face from objects, respectively. From the assumption we devised a bilateral filter using color and depth and incorporate it into the mean-shift algorithm. We demonstrated the proposed method by some experiments.

• The Journal of the Acoustical Society of Korea
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• v.29 no.3
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• pp.200-208
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• 2010

In this work, the minimum statistics (MS) algorithm is combined with the codebook driven short-term predictor parameter estimation (CDSTP) to design a speech enhancement algorithm that is robust against various background noise environments. The MS algorithm functions well for the stationary noise but relatively not for the non-stationary noise. The CDSTP works efficiently for the non-stationary noise, but not for the noise that was not considered in the training stage. Thus, we propose to combine CDSTP and MS. Compared with the single use of MS and CDSTP, the proposed method produces better perceptual evaluation of speech quality (PESQ) score, and especially works excellent for the mixed background noise between stationary and non-stationary noises.

• The Journal of the Acoustical Society of Korea
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• v.25 no.2
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• pp.78-87
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• 2006

In this paper. we propose an effective mask estimation scheme for missing-feature reconstruction in order to achieve robust speech recognition under unknown noise environments. In the previous work. colored noise is used for training the mask classifer, which is generated from the entire frequency Partitioned signals. However it gives a limited performance under the restricted number of training database. To reflect the spectral events of more various background noise and improve the performance simultaneously. a new Bayesian classifier for mask estimation is proposed, which works independent of other frequency bands. In the proposed method, we employ the colored noise which is obtained by combining colored noises generated from each frequency band in order to reflect more various noise environments and mitigate the 'sparse' database problem. Combined with the cluster-based missing-feature reconstruction. the performance of the proposed method is evaluated on a task of noisy speech recognition. The results show that the proposed method has improved performance compared to the Previous method under white noise. car noise and background music conditions.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.2
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• pp.226-231
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• 2007

This paper presents a method of colored object extraction from an image using the fuzzy neural network. Fuzzy neural network divides an image into two clusters. It extracts the prototypes of Cb and Cr of object and background by controlling the vigilance parameter. The proposed method extracted object regardless of the position, the size, and the intensity of object. We compared the performance of the proposed method with that of the method of using subjective threshold value. And, we compared the performance of the proposed method with that of the method of using subjective threshold value by using several images with added noises.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.196-201
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• 2004

In biometric and biomedical applications, a special transporting mechanism must be designed for the ${\mu}$TAS (micro total analysis system) to move samples and reagents through the microchannels that connect the unit procedure components in the system. An important issue for this miniaturization and integration is microfluid management technique, i.e., microfluid transportation, metering, and mixing. In view of this, this study presents an optimal fuzzy sliding-mode control (OFSMC) design based on the 8051 microprocessor and implementation of a complete microfluidic manipulated system implementation of biochip system with a pneumatic pumping actuator, a feedback-signal photodiodes and flowmeter. The new microfluid management technique successfully improved the efficiency of molecular biology reaction by increasing the velocity of the target nucleic acid molecules, which increases the effective collision into the probe molecules as the target molecules flow back and forth. Therefore, this hybridization chip was able to increase hybridization signal 6-fold and reduce non-specific target-probe binding and background noises within 30 minutes, as compared to conventional hybridization methods, which may take from 4 hours to overnight. In addition, the new technique was also used in DNA extraction. When serum existed in the fluid, the extraction efficiency of immobilized beads with solution flowing back and forth was 88-fold higher than that of free-beads.

• Journal of Korean Society for Atmospheric Environment
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• v.19 no.4
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• pp.397-414
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• 2003

The performance of one-particle stochastic Lagrangian models for passive tracer dispersion are evaluated against measurements in horizontally-homogeneous neutrally-stratified atmospheric surface layer. State-of-the-technology models as well as classical Langevin models, all in class of well mixed models are numerically implemented for inter-model comparison study. Model results (far-downstream asymptotic behavior and vertical profiles of the time averaged concentrations, concentration fluxes, and concentration fluctuations) are compared with the reported measurements. The results are: 1) the far-downstream asymptotic trends of all models except Reynolds model agree well with Garger and Zhukov's measurements. 2) profiles of the average concentrations and vertical concentration fluxes by all models except Reynolds model show good agreement with Raupach and Legg's experimental data. Reynolds model produces horizontal concentration flux profiles most close to measurements, yet all other models fail severely. 3) With temporally correlated emissions, one-particle models seems to simulate fairly the concentration fluctuations induced by plume meandering, when the statistical random noises are removed from the calculated concentration fluctuations. Analytical expression for the statistical random noise of one-particle model is presented. This study finds no indication that recent models of most delicate theoretical background are superior to the simple Langevin model in accuracy and numerical performance at well.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.2 s.314
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• pp.37-45
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• 2007

We propose an adaptive image enhancement method for epigraphic images, which is based on local statistics. Local statistics of the image are utilized for adaptive realization of the enhancement, that controls the contribution of the smoothing or sharpening paths. Image contrast enhancement occurs in details and noises are suppressed in smooth areas. For modeling the epigraphic image, pre~process is achieved by HSDI(Hanzi squeezed digital image). We have calculated the local statistics from this HSDI model. Application of this approach to HSDI has shown that processing not only smooths the background areas but also improves the subtle variations of edges, so that the word regions can be enhanced. Experimental results show that the proposed algorithm has better performance than the conventional image enhancement ones.

• Progress in Superconductivity and Cryogenics
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• v.5 no.1
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• pp.21-26
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• 2003

We designed and constructed a multichannel superconducting quantum interference device (SQUID) magnetometer system to measure magnetic fields from the human brain. We used a new type of SQUID, the double relaxation oscillation SQUID (DROS). With high flux-to-voltage transfers of the DROS, about 10 times larger than the dc SQUIDs, simple flux-locked loop circuits could be used for SQUID operation. Also the large modulation voltage of the DROS, typically being 100 $mutextrm{V}$, enabled stable flux-locked loop operation against the thermal offset voltage drift of the preamplifier. The magnetometers were fabricated using the Nb/AlOx/Nb junction technology. The SQUID system consists of 37 signal magnetometers, distributed on a semispherical surface, and 11 reference channels were installed to pickup background noises. External feedback was used to eliminate the magnetic coupling with the adjacent channels. The liquid helium dewar has a capacity of 29 L and boil-off rate of about 4 L/d with the total 48 channel insert. The magnetometer system has an average noise level of 3 fT/√Hz at 100 Hz, inside a shielded loon, and was applied to measure auditory-evoked fields.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.4
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• pp.817-822
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• 2011

This paper first proposes the speech recognition algorithm by detection of the speech and noise sections at each frame using a neural network training by back-propagation algorithm, then proposes the spectral subtraction method which removes the noises at each frame according to detection of the speech and noise sections. In this experiment, the performance of the proposed recognition system was evaluated based on the recognition rate using various speeches that are degraded by white noise and car noise. Moreover, experimental results of the noise reduction by the spectral subtraction method demonstrate using the speech and noise sections detecting by the speech recognition algorithm at each frame. Based on measuring signal-to-noise ratio, experiments confirm that the proposed algorithm is effective for the speech by corrupted the noise using signal-to-noise ratio.