• Korean Journal of Fisheries and Aquatic Sciences
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• v.55 no.2
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• pp.195-206
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• 2022

The separation of spectrally overlapped broadband echo signals from free-swimming Japanese needlefish Hypohamphus sajori using the fractional Fourier transform (FrFT) was investigated. The broadband echo signals were measured over frequency ranges of 40-80 and 110-220 kHz. The overlapped echo signals were separated after eliminating noise signals in the smoothed pseudo-Wigner-Ville distribution domain. The echo signal from a 40 mm WC sphere suspended just below a chirp transducer was used to calibrate the broadband of the chirp echo sounder and estimate the frequency dependence of target strength for the separated echo signals. The experimental results show that the proposed FrFT method can analyze the time-frequency image of broadband echo signals from free-swimming individual fish effectively and can be used as a quantitative tool for extracting the acoustic features used for fish species identification.

• MALSORI
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• no.65
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• pp.153-168
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• 2008

We propose a new method to apply shape analysis techniques to a computational auditory scene analysis (CASA)-based speech separation system. The conventional CASA-based speech separation system extracts speech signals from a mixture of speech and noise signals. In the proposed method, we complement the missing speech signals by applying the shape analysis techniques such as labelling and distance function. In the speech separation experiment, the proposed method improves signal-to-noise ratio by 6.6 dB. When the proposed method is used as a front-end of speech recognizers, it improves recognition accuracy by 22% for the speech-shaped stationary noise condition and 7.2% for the two-talker noise condition at the target-to-masker ratio than or equal to -3 dB.

• The Journal of the Acoustical Society of Korea
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• v.29 no.2
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• pp.141-147
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• 2010

In this paper, we propose a new speech separation algorithm to extract and enhance the target speech signals from mixed speech signals by utilizing both magnitude and phase information. Since the previous statistical modeling algorithms assume that the log power spectrum values of the mixed speech signals are independent in the temporal and frequency domain, discontinuities occur in the resultant separated speech signals. To reduce the discontinuities, we apply a smoothing filter in the time-frequency domain. To further improve speech separation performance, we propose a statistical model based on both magnitude and phase information of speech signals. Experimental results show that the proposed algorithm improve signal-to-interference ratio (SIR) by 1.5 dB compared with the previous magnitude-only algorithms.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.474-477
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• 2005

In tactical theater, it is crucial to detect ground moving targets and to locate them precisely. This problem can be resolved by using SAR (Synthetic Aperture Radar) sensors providing GMTI (Ground Moving Target Indication) capability. In general, to implement a robust GMTI sensor is not simple because of the strong competitions between target signals and clutter signals on the ground, and low speed of moving targets. Contrary to the case that a delay canceller is mostly suitable for ground surveillance radars, DPCA (Displaced Phase Centered Antenna) or STAP (Space Time Adaptive Processing) techniques have been adapted for GMT! function of modem airborne radars. In this paper, anew scheme of DPCA using two passive antennas with vertical separation is proposed, which also provides good clutter cancellation performance. The proposed scheme enables us to scan straight ahead of the carrying platform that is impossible with typical DPCA configuration. Simulations using various conditions have been performed to validate the proposed scheme, and the results are acceptable.

• Korean Journal of Remote Sensing
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• v.22 no.3
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• pp.229-234
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• 2006

In tactical theater, it is crucial to detect ground moving targets and to locate them precisely. This problem can be resolved by using SAR (Synthetic Aperture Radar) sensors providing GMTI (Ground Moving Target Indication) capability. In general, to implement a robust GMTI sensor is not simple because of the strong competitions between target signals and clutter signals from the ground, and low speed of moving targets. Contrary to the case that a delay canceller is mostly suitable for ground surveillance radars, DPCA (Displaced Phase Centered Antenna) or STAP (Space Time Adaptive Processing) techniques have been widely adapted for GMTI function of modern airborne radars. In this paper, a new scheme of DPCA using two passive antennas with vertical separation is proposed, which also provides good clutter cancellation performance. The proposed scheme realizes full azimuth coverage for DPCA operation on an airborne platform, which is impossible with classical DPCA configuration. Simulations using various conditions have been performed to validate the proposed scheme, and the results are acceptable.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.9
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• pp.838-842
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• 2007

A brain-computer interface(BCI) system is a communication channel which transforms a subject's thought process into command signals to control various devices. These systems use electroencephalographic signals or the neuronal activity of many single neurons. The presented study deals with an efficient analysis method of neuronal signals from a BCI System using an independent component analysis(ICA) algorithm. The BCI system was implemented to generate event signals coding movement information of the subject. To apply the ICA algorithm, we obtained the perievent histograms of neuronal signals recorded from prefrontal cortex(PFC) region during target-to-goal(TG) task trials in the BCI system. The neuronal signals were then smoothed over 5ms intervals by low-pass filtering. The matrix of smoothed signals was then rearranged such that each signal was represented as a column and each bin as a row. Each column was also normalized to have a unit variance. As a result, we verified that different patterns of the neuronal signals are dependent on the target position and predefined event signals.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.5
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• pp.807-812
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• 2020

This paper proposes a blind speech source separation algorithm using a microphone to separate only the target speech source signal in an environment in which various speech source signals are mixed. The proposed algorithm is a model of frequency domain representation based on independent component analysis method. Accordingly, for the purpose of verifying the validity of independent component analysis in the frequency domain for two speech sources, the proposed algorithm is executed by changing the type of speech sources to perform speech sources separation to verify the improvement effect. It was clarified from the experimental results by the waveform of this experiment that the two-channel speech source signals can be clearly separated compared to the original waveform. In addition, in this experiments, the proposed algorithm improves the speech source separation performance compared to the existing algorithms, from the experimental results using the target signal to interference energy ratio.

• The Journal of the Acoustical Society of Korea
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• v.38 no.2
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• pp.177-183
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• 2019

In this paper, we proposed an application method of ICA (Independent Component Analysis) to passive line array sonar to separate interferences from target signals in low frequency band and compared performance of three conventional ICA algorithms. Since the low frequency signals are received through larger bearing angles than other frequency bands, neighboring beam signals can be used to perform ICA as measurement signals of the ICA. We use three ICA algorithms such as Fast ICA, NNMF (Non-negative Matrix Factorization) and JADE (Joint Approximation Diagonalization of Eigen-matrices). Through experiments on real data obtained from passive line array sonar, it is verified that the interference can be separable from target signals by the suggested method and the JADE algorithm shows the best separation performance among the three algorithms.

• The Journal of the Acoustical Society of Korea
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• v.35 no.2
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• pp.110-117
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• 2016

Passive sonar systems detect and classify the target by analyzing the radiated noises from vessels. If multiple noise sources exist within the sonar detection range, it gets difficult to classify each noise source because mixture of noise sources are observed. To overcome this problem, a beamforming technique is used to separate noise sources spatially though it has various limitations. In this paper, we propose a new method that uses a FDICA (Frequency Domain Independent Component Analysis) to separate noise sources from the mixture. For experiments, each noise source signal was synthesized by considering the features such as machinery tonal components and propeller tonal components. And the results of before and after separation were compared by using LOFAR (Low Frequency Analysis and Recording), DEMON (Detection Envelope Modulation On Noise) analysis.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.236-239
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• 1999

Target signal feature parameters are very important to classify target by active sonar. Two highly correlated broadband pulses separated by time T have a time separation pitch (TSP) of 1/THz, equal to the spacing between ripples of its spectrum. In this study, TSP is applied to scaled-target echoes to be used as a feature parameter. The TSP from the target sign리 when source signals are CW short, CW long, and LFM long was investigated. It is also found the TSP can be applied to the target signal with doppler shift. It is shown that the position and magnitude of highlight can be found for LSEM based on TSP.