• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.4
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• pp.118-127
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• 1998

In this paper, we propose a voice personality transformation method which makes one person's voice sound like another person's voice. In order to transform the voice personality, vocal tract transfer function is used as a transformation parameter. Comparing with previous methods, the proposed method can obtain high-quality transformed speech with low computational complexity. Conversion between the vocal tract transfer functions is implemented by a linear mapping based on soft clustering. In this process, mean LPC cepstrum coefficients and mean removed LPC cepstrum modeled by the low dimensional vector are used as transformation parameters. To evaluate the performance of the proposed method, mapping rules are generated from 61 Korean words uttered by two male and one female speakers. These rules are then applied to 9 sentences uttered by the same persons, and objective evaluation and subjective listening tests for the transformed speech are performed.

• ETRI Journal
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• v.41 no.3
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• pp.316-325
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• 2019

Accurate suppressive jamming is a prominent problem faced by radar equipment. It is difficult to solve signal detection problems for extremely low signal to noise ratios using traditional signal processing methods. In this study, a joint sensing dictionary based compressed sensing and adaptive iterative optimization algorithm is proposed to counter suppressive jamming in information domain. Prior information of the linear frequency modulation (LFM) and suppressive jamming signals are fully used by constructing a joint sensing dictionary. The jamming sensing dictionary is further adaptively optimized to perfectly match actual jamming signals. Finally, through the precise reconstruction of the jamming signal, high detection precision of the original LFM signal is realized. The construction of sensing dictionary adopts the Pei type fast fractional Fourier decomposition method, which serves as an efficient basis for the LFM signal. The proposed adaptive iterative optimization algorithm can solve grid mismatch problems brought on by undetermined signals and quickly achieve higher detection precision. The simulation results clearly show the effectiveness of the method.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.5
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• pp.26-33
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• 1994

Phase retrieval is concerned with the reconstruction of a signal from its Fourier transform magnitude (or intensity), which arises in many areas such as X-ray crystallography, optics, astronomy, or digital signal processing. In such areas, the Fourier transform phase of the desired signal is lost while measuring Fourier transform magnitude (F.T.M.). However, if a reference 'signal is added to the desired signal, then, in the Fourier trans form magnitude of the added signal, the Fourier transform phase of the desired signal is encoded. This paper addresses uniqueness and retrieval of the encoded Fourier phase of a multidimensional signal from the Fourier transform magnitude of the added signal along with the Fourier transform magnitude of the desired signal and the information of the additive reference signal. In Part I, several conditions under which the desired signal can be uniquely specified from the two Fourier transform magnitudes and the additive reference signal are presented. In Part II, the development of non-iterative algorithms and an iterative algorithm that may be used to reconstruct the desired signal(s) is considered.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.5
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• pp.34-41
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• 1994

Phase retrieval is concerned with the reconstruction of a signal from its Fourier transform magnitude (or intensity), which arises in many areas such as X-ray crystallography, optics, astronomy, or digital signal processing In such areas, the Fourier transform phase of the desired signal is lost while measuring Fourier transform magnitude (F.T.M.). However, if a reference 'signal is added to the desired signal, then, in the Fourier trans form magnitude of the added signal, the Fourier transform phase of the desired signal is encoded This paper addresses uniqueness and retrieval of the encoded Fourier phase of a multidimensional signal from the Fourier transform magnitude of the added signal along with Fourier transform magnitude of the desired signal and the information of the additive reference signal In Part I, several conditions under which the desired signal can be uniquely specified from the two Fourier transform magnitudes and the additive reference signal are presented In Part II, the development of non-iterative algorithms and an iterative algorithm that may be used to reconstruct the desired signal (s) is considered

• Proceedings of the IEEK Conference
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• 2000.06e
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• pp.117-120
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• 2000

Pulse oximetry is a well established non-invasive optical technique for monitoring the SpO$_2$ during anaesthesia, recovery and intensive care. Pulse oximeters determine the oxygen saturation level of blood by measuring the light absorption of arterial blood. The sensors consists of red and infrared light sources and photodetectors. In the measurement of the hemoglobin oxygen saturation, conventional method has required the technique of filtering of remove the noise, and of complex signal processing algorithm. So much time have required to signal processing. In this research, we separate AC signal and DC signal in the stage of signal detection. We filter the noise from each signal and convert A/D. We obtain the SpO$_2$ using the DSP algorithm.

• Proceedings of the KOSOMBE Conference
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• v.1995 no.05
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• pp.183-185
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• 1995

We present a visual programming system for signal analysis; it allows the user to construct signal processing algorithms by assembling visually fundamental signal processing blocks, and to observe the processed signals as well as the original signals in detail by magnifying a portion and measuring the time interval and amplitude between two points. Each fundamental signal processing block is implemented as an independent dynamically linked library module. Therefore, the user can expand the system processing capability by simply adding dynamically linked library modules without restructuring the entire system.

• The Journal of the Acoustical Society of Korea
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• v.36 no.2
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• pp.123-129
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• 2017

In this paper, the realization of a directional digital hearing aid was considered. Conventional time domain time delay method was replaced with digital filters in order to make any general-purposed DSP (Digital Signal Processing) chip to produce the similar directivity pattern. Both the time delay algorithm and the digital filter algorithm were initially evaluated by Matlab (Matrix laboratory) for comparison, and it was confirmed by CSR 8675 Bluetooth DSP IC (Digital Signal Processing Integrated Circuit) chip firmware realization. Some remote control features by a smart phone was added to the smart hearing aid for user interface easiness.

• The Magazine of the IEIE
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• v.12 no.5
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• pp.47-52
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• 1985

• Journal of IKEEE
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• v.22 no.4
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• pp.1044-1049
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• 2018

CNN (Convolutional Neural Networks) is a neural network that models animal visual information processing. And it shows good performance in various fields. In this paper, we use CNN to classify target and non-target data by analyzing the spectrogram of active sonar signal. The data were divided into 8 classes according to the ratios containing the targets and used for learning CNN. The spectrogram of the signal is divided into frames and used as inputs. As a result, it was possible to classify the target and non-target using the characteristic that the classification results of the seven classes corresponding to the target signal sequentially appear only at the position of the target signal.

• Journal of Sensor Science and Technology
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• v.23 no.1
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• pp.1-6
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• 2014

In many medical image devices, dc noise often prevents normal diagnosis. In wireless capsule endoscopy systems, multipath fading through indoor wireless links induces inter-symbol interference (ISI) and indoor electric devices generate impulsive noise in the received signal. Moreover, dc noise, ISI, and impulsive noise are also found in optical fiber communication that can be used in remote medical diagnosis. In this paper, a blind signal processing method based on the biased probability density functions of constant modulus error that is robust to those problems that can cause error propagation in decision feedback (DF) methods is presented. Based on this property of robustness to error propagation, a DF version of the method is proposed. In the simulation for the impulse response of optical fiber channels having slowly varying dc noise and impulsive noise, the proposed DF method yields a performance enhancement of approximately 10 dB in mean squared error over its linear counterpart.