• ETRI Journal
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• v.32 no.6
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• pp.921-931
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• 2010

It has been shown that the principal subspace-based multichannel Wiener filter (MWF) provides better performance than the conventional MWF for suppressing interference in the case of a single target source. It can efficiently estimate the target speech component in the principal subspace which estimates the acoustic transfer function up to a scaling factor. However, as the input signal-to-interference ratio (SIR) becomes lower, larger errors are incurred in the estimation of the acoustic transfer function by the principal subspace method, degrading the performance in interference suppression. In order to alleviate this problem, a principal subspace modification method was proposed in previous work. The principal subspace modification reduces the estimation error of the acoustic transfer function vector at low SIRs. In this work, a frequency-band dependent interpolation technique is further employed for the principal subspace modification. The speech recognition test is also conducted using the Sphinx-4 system and demonstrates the practical usefulness of the proposed method as a front processing for the speech recognizer in a distant-talking and interferer-present environment.

• The Journal of the Acoustical Society of Korea
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• v.39 no.5
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• pp.490-496
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• 2020

We present a method to improve the performance of the multi-channel Wiener filter in noisy environment. To build subspace-based multi-channel Wiener filter, in the case of single target source, the target speech component can be effectively estimated in the principal subspace of speech correlation matrix. The speech correlation matrix can be estimated by subtracting noise correlation matrix from signal correlation matrix based on the assumption that the cross-correlation between speech and interfering noise is negligible compared with speech correlation. However, this assumption is not valid in the presence of strong interfering noise and significant error can be induced in the principal subspace accordingly. In this paper, we propose to adjust the principal subspace vector using speech presence probability and the steering vector for the desired speech source. The multi-channel speech presence probability is derived in the principal subspace and applied to adjust the principal subspace vector. Simulation results show that the proposed method improves the performance of multi-channel Wiener filter in noisy environment.

• Proceedings of the IEEK Conference
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• 2000.06a
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• pp.205-208
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• 2000

In this paper, we design Tracking Filter that is principal component of Frequency Hopping System. This filter can acquire hopping pattern in short time and track it at high speed. This is high Q, narrowband, RF filter whose center frequency is controlled digitally between 30MHz ∼ 88MHz.

• Communications of the Korean Mathematical Society
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• v.14 no.2
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• pp.287-293
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• 1999

In this paper, we give an affirmative answer to the question raised in [5]; whether L((P)) is principal or not. Using this fact, we try to give concrete form of module of quotient with respect to a torsion theory determined by L((P)).

• MALSORI
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• no.53
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• pp.93-102
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• 2005

The performance of a speech recognition system is generally degraded in telephone environment because of distortions caused by background noise and various channel characteristics. In this paper, data-driven temporal filters are investigated to improve the performance of a specific recognition task such as telephone speech. Three different temporal filtering methods are presented with recognition results for Korean connected-digit telephone speech. Filter coefficients are derived from the cepstral domain feature vectors using the principal component analysis. According to experimental results, the proposed temporal filtering method has shown slightly better performance than the previous ones.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.29 no.12
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• pp.1-7
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• 2015

In this paper, we propose a second-order fiber flexible comb filter based on a polarization-diversity loop(PDL). The proposed filter consists of a polarization beam splitter, four half-wave plates(HWPs), and three high birefringence fiber(HBF) segments. In the previous Solc-type second-order filter based on the PDL, HBF segments were fusion-spliced with a fixed angle offset between their principal axes with each other. But, the proposed filter implemented by inserting two HWPs between three HBF segments has a great flexibility in adjusting relative angular difference between the principal axes of two adjacent HBF segments. Owing to this flexibility, second-order transmission spectra, which had a channel spacing of ~0.8nm, could be interleaved by controlling the orientation angles of four HWPs. The output transmission spectra of the proposed filter were theoretically analyzed and experimentally verified.

• Journal of the Optical Society of Korea
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• v.12 no.3
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• pp.147-151
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• 2008

This paper proposes a new polarization-independent Sagnac birefringence loop structure-based multiwavelength-periodic filter and presents measurements and analysis of its spectrum. The filter can be used in several schemes by adjusting the orientation angles of two quarter waveplates and the operating characteristics in the reflection type are analyzed including dispersion and polarization mode dispersion at each principal axis. This filter has polarization-independent spectra but a polarization-dependent dispersion, consequently polarization mode dispersion whose values changes with operating schemes.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.764-767
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• 2004

The performance of a speech recognition system is generally degraded in telephone environment because of distortions caused by background noise and various channel characteristics. In this paper, data-driven temporal filters are investigated to improve the performance of a specific recognition task such as telephone speech. Three different temporal filtering methods are presented with recognition results for Korean connected-digit telephone speech. Filter coefficients are derived from the cepstral domain feature vectors using the principal component analysis.

• Journal of Korea Multimedia Society
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• v.10 no.1
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• pp.26-35
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• 2007

We present a robust face tracking system from the sequence of video images based on effective detector and Kalman filter. To construct the effective face detector, we extract the face features using the five types of simple Haar-like features. Extracted features are reinterpreted using Principal Component Analysis (PCA), and interpreted principal components are used for Support Vector Machine (SVM) that classifies the faces and non-faces. We trace the moving face with Kalman filter, which uses the static information of the detected faces and the dynamic information of changes between previous and current frames. To make a real-time tracking system, we reduce processing time by adjusting the frequency of face detection. In this experiment, the proposed system showed an average tracking rate of 95.5% and processed at 15 frames per second. This means the system is robust enough to track faces in real-time.

• Journal of Information Technology Services
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• v.6 no.3
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• pp.241-249
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• 2007

This paper describes a real-time face tracking system using effective detector and Kalman filter. In the proposed system, an image is separated into a background and an object using a real-time updated face color for effective face detection. The face features are extracted using the five types of simple Haar-like features. The extracted features are reinterpreted using Principal Component Analysis (PCA), and interpreted principal components are used for Support Vector Machine (SVM) that classifies the faces and non-faces. The moving face is traced with Kalman filter, which uses the static information of the detected faces and the dynamic information of changes between previous and current frames. The proposed system sets up an initial skin color and updates a region of a skin color through a moving skin color in a real time. It is possible to remove a background which has a similar color with a skin through updating a skin color in a real time. Also, as reducing a potential-face region using a skin color, the performance is increased up to 50% when comparing to the case of extracting features from a whole region.