• The Journal of Korean Institute of Communications and Information Sciences
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• v.12 no.5
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• pp.466-477
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• 1987

본 논문에서는 NTSC칼라 TV합성신호를 프레임내에서 색도신호와 명도신호성분으로 분리하기 위한 두 종류의 적응 필터링 방식을 제시하였다. 적응 필터링 방식에 있어서 합성신호는 수직필터와 수평필터에 의하여 필터링 되어 지고 화상의 국부적인 특성에 따라 필터의 출력이 선택되어 진다. 첫번째 방식에서는 조건형 스위칭 알고리즘에 의하여 수직필터 또는 수평필터의 출력이 최종단의 출력으로 결정되어 진다. 두번째 방식에서는 선형 조합 검출 알고리즘에 의하여 수직필터와 수평필터의 출력의 가중치합이 최종단의 출력이 된다. 사용된 필터들은 NTSC 칼라 TV 합성신호를 4fsc로 샘플한 경우에 대하여 설계되어졌다. 몇가지 정량적인 기준을 이용하여 여러가지 방식들을 컴퓨터 시뮬레이션에 의하여 비교평가하였다.

• The KIPS Transactions:PartC
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• v.8C no.3
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• pp.359-366
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• 2001

이 논문에서는 주파수 비선택적 느린 나카가미 m-분포 페이딩 육상 이동통신 채널에서 M 진 QAM 신호에 L-가지의 최대비 합성 다이버시티를 적용하였을 때의 오율성능을 유도하 고 분석한다. 다이버시티 채널의 페이딩 인자가 동일한 경우와 서로 다른 경우에 대하여 각 각 다중가지 다이버시티 시스템을 고려한다. 페이딩 지수 m=1 인 경우 유도된 M진 QAM 신호의 최대비 합성 다이버시티 심볼 오율식은 레일리 페이딩 채널에서의 오류식으로 돌아 간다. 유도된 결과 식들은 L 값에 대하여 신호대 잡음비에 대한 함수로 최대비 합성 다이버 시티 심볼 오율을 나타낸다. L 값을 증가시킬수록 성능 향상을 가져옴을 보여준다. 이 논문 에서 제시된 결과들은 디지털 육상 이동통신에서 QAM 시스템의 성능을 편리하게 셰산하는 데 충분히 일반적이다.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.13 no.2
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• pp.107-114
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• 2002

The problem of blind signal separation of independent sources consist in retrieving the source from the observation of unknown mixtures of unknown sources. In this paper, we propose a technique for blind signal separation that can extract original signals from their non-stationary mixtures observed in a ordinary room. The proposed method implements blind signal separation by minimizing a non-negative cost function that achieves the minimum when the second-order cross-correlation value of the observed signals becomes zero. The validity of the proposed method has been verified by a computer simulation and experiment that extracts two source signals from their mixtures observed in a normal room.

• The Journal of the Acoustical Society of Korea
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• v.18 no.2
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• pp.77-82
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• 1999

A sinusoidal model has been widely used in the analysis and synthesis of speech and audio signals, and becomes one of the efficient candidates for high quality low bit rate audio coders. One of the crucial steps in the analysis and synthesis using a sinusoidal model is the detection of tonal components. This paper proposes an efficient method for the analysis and synthesis of audio signals using a sinusoidal model, which uses psychoacoustic criteria such as masking effect, masking index, and JNDf(Just Noticeable Difference in Frequency). Simulation results show that the proposed method reduces the number of sinusoids significantly without degrading the quality of the synthesized audio signals.

• The KIPS Transactions:PartB
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• v.9B no.2
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• pp.223-230
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• 2002

In a speech coding system using excitation source of voiced and unvoiced, it would be involves a distortion of speech waveform in case coexist with a voiced and an unvoiced consonants in a frame. This paper present a new method of TSIUVC (Transition Segment Including Unvoiced Consonant) approximate-synthesis by using Least Mean Square. The TSIUVC extraction is based on a zero crossing rate and IPP (Individual Pitch Pulses) extraction algorithm using residual signal of FIR-STREAK Digital Filter. As a result, This method obtain a high Quality approximation-synthesis waveform by using Least Mean Square. The important thing is that the frequency signals in a maximum error signal can be made with low distortion approximation-synthesis waveform. This method has the capability of being applied to a new speech coding of Voiced/Silence/TSIUVC, speech analysis and speech synthesis.

• The Journal of the Acoustical Society of Korea
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• v.20 no.2
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• pp.44-51
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• 2001

This Paper the practical echo signal synthesis model to predict the target strength and signal shape of a submarine for a valuable tool to active sonar engineer. It is based on UTAHID (Underwater TArget by Highlight Distribution) model which is relocated highlight points along to external hull for aspect angle, and synthesized echo signal by modified grouping highlights to internal scatter cloud. Proposed model is analyzed target strength characteristics on various incident pulse length, and synthesis signal signature, target time spreading loss, echo elongation effect and so on. Thus it can be efficiently used in various real systems related to underwater target echo signal synthesis, that is, active sonar, acoustic countermeasure and surveillance system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.4
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• pp.501-507
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• 2020

This paper proposes a new frame synchronization technique based on convolutional neural network (CNN). The conventional frame synchronizers usually find the matching instance through correlation between the received signal and the preamble. The proposed method converts the 1-dimensional correlator ouput into a 2-dimensional matrix. The 2-dimensional matrix is input to a convolutional neural network, and the convolutional neural network finds the frame arrival time. Specifically, in additive white gaussian noise (AWGN) environments, the received signals are generated with random arrival times and they are used for training data of the CNN. Through computer simulation, the false detection probabilities in various signal-to-noise ratios are investigated and compared between the proposed CNN-based technique and the conventional one. According to the results, the proposed technique shows 2dB better performance than the conventional method.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.06c
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• pp.255-258
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• 1998

Sine파를 이용한 오디오 분석과 합성은 고음질 저비트율 오디오 부호화에 매우 효율적인 방법의 하나로 알려져 있다. 본 논문은 sine파를 이용한 오디오 분석과 합성에 중요한 sine파 검출에 심리음향모델을 활용하는 방안을 제안하였다. 모의실험 결과, 심리음향모델을 사용한 경우 사용하지 않은 경우에 비해 합성에 사용되는 sine파의 개수를 약 50% 정도 줄일 수 있었음을 알 수 있었다. 한편 오디로 신호의 attack이나 nonstationarity를 처리할 수 있는 방법이 sine파를 이용한 오디오 부호화에 필수적이라는 사실을 확인하였고 그에 대한 대처 방안을 제시하였다.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.2
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• pp.90-96
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• 2023

Securing large amounts of training data in deep learning neural networks, including convolutional neural networks, is of importance for avoiding overfitting phenomenon or for the excellent performance. However, securing labeled training data in deep learning neural networks is very limited in reality. To overcome this, several augmentation methods have been proposed in the literature to generate an additional large amount of training data through transformation or manipulation of the already acquired traing data. However, unlike training data such as images and texts, it is barely to find an augmentation method in the literature that additionally generates bio-signal training data for convolutional neural network based human activity recognition. Thus, this study proposes a simple but effective augmentation method of bio-signal training data for convolutional neural network based human activity recognition. The usefulness of the proposed augmentation method is validated by showing that human activity is recognized with high accuracy by convolutional neural network trained with its augmented bio-signal training data.

• The Journal of the Acoustical Society of Korea
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• v.14 no.3
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• pp.49-60
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• 1995

This paper is a study on the design and implementation of the Korean Text-to-Speech system which is used for a small and simple system. In this paper, a parameter synthesis method is chosen for speech syntheiss method, we use PARCOR(PARtial autoCORrelation) coefficient which is one of the LPC analysis. And we use phoneme for synthesis unit which is the basic unit for speech synthesis. We use PARCOR, pitch, amplitude as synthesis parameter of voice, we use residual signal, PARCOR coefficients as synthesis parameter of unvoice. In this paper, we could obtain the 60% intelligibility by using the residual signal as excitation signal of unvoiced sound. The result of synthesis experiment, synthesis of a word unit is available. The controlling of phoneme duration is necessary for synthesizing of a sentence unit. For setting up the synthesis system, PC 486, a 70[Hz]-4.5[KHz] band pass filter for speech input/output, amplifier, and TMS320C30 DSP board was used.