• The Journal of the Acoustical Society of Korea
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• v.43 no.3
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• pp.324-334
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• 2024

The Wavefront Curvature Ranging (WCR) is an estimation method for a source range from the wavefront curvature of acoustic waves. The conventional method uses trigonometry to estimate the source range by assuming the sound speed as a constant. Because of this assumption, range error occurs in the ocean environment where the bottom reflection is clearly separated. In order to reduce the range error, Matched Wavefront Curvature Ranging (MWCR) was proposed applying the sound speed structure in the ocean environment and Maximum Likelihood Estimation (MLE). The range error was reduced in the results of the simulation on the proposed method. In the future, this method will be applicable to the sonar system if the reliability of ranging is confirmed by measured signal.

• The Korean Journal of Applied Statistics
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• v.30 no.6
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• pp.889-899
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• 2017

Independent component analysis is a multivariate approach to separate mixed signals into original signals. It is the most widely used method of blind source separation technique. ICA uses linear transformations such as principal component analysis and factor analysis, but differs in that ICA requires statistical independence and non-Gaussian assumptions of original signals. PCA have a natural ordering based on cumulative proportion of explained variance; howerver, ICA algorithms cannot identify the unique optimal ordering of the components. It is meaningful to set order because major components can be used for further analysis such as clustering and low-dimensional graphs. In this paper, we compare the performance of several criteria to determine the order of the components. Kurtosis, absolute value of kurtosis, negentropy, Kolmogorov-Smirnov statistic and sum of squared coefficients are considered. The criteria are evaluated by their ability to classify known groups. Two types of data are analyzed for illustration.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.5
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• pp.811-816
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• 2019

This paper first proposes a technique to separate clean speech signals and mixed noise signals by using an independent vector analysis algorithm of frequency band for 4 channel speech source signals with a noise. An improved output speech signal from the proposed independent vector analysis algorithm is obtained by using the cross-correlation between the signal outputs from the frequency domain delay-sum beamforming and the output signals separated from the proposed independent vector analysis algorithm. In the experiments, the proposed algorithm improves the maximum SNRs of 10.90dB and the segmental SNRs of 10.02dB compared with the frequency domain delay-sum beamforming algorithm for the input mixed noise speeches with 0dB and -5dB SNRs including white noise, respectively. Therefore, it can be seen from this experiment and consideration that the speech quality of this proposed algorithm is improved compared to the frequency domain delay-sum beamforming algorithm.

• The Journal of the Acoustical Society of Korea
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• v.34 no.3
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• pp.227-233
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• 2015

In this paper, we propose music and voice separation using kernel sptectrogram models backfitting based on log-spectral amplitude estimator. The existing method separates sources based on the estimate of a desired objects by training MSE (Mean Square Error) designed Winer filter. We introduce rather clear music and voice signals with application of log-spectral amplitude estimator, instead of adaptation of MSE which has been treated as an existing method. Experimental results reveal that the proposed method shows higher performance than the existing methods.

• The Journal of the Korea Contents Association
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• v.7 no.8
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• pp.1-12
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• 2007

This paper presents a new demixing method that separates each source from a stereo sound mixture. Under the W-Disjoint Orthogonal assumption in DUET(Degenerate Unmixing Estimation Technique) algorithm. The proposed method is mainly processed in time-frequency domain by using windowed-fourier transform. In this paper there are two main contributions: a weighted mask by panning index distances and a binary mask by comparing each channel value. The former has tender demixing characteristic, and the latter has stronger demixing characteristic. In experimental results, we will show that both masks produce more robust demixing than the existing demixing methods do.

• 한국약용작물학회:학술대회논문집
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• 2009.05a
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• pp.255-256
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• 2009

• Journal of Broadcast Engineering
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• v.23 no.4
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• pp.519-525
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• 2018

This paper deals with noise suppression of multi-channel data captured by microphone array using multi-channel Wiener filter. Multi-channel Wiener filter does not rely on information about the direction of the target speech and can be partitioned into an MVDR (Minimum Variance Distortionless Response) spatial filter and a single channel spectral filter. The acoustic transfer function between the single speech source and microphones can be estimated by subspace decomposition of multi-channel Wiener filter. The errors are incurred in the estimation of the acoustic transfer function due to the errors in the estimation of correlation matrices, which in turn results in speech distortion in the MVDR filter. To alleviate the speech distortion in the MVDR filter, diagonal loading is applied. In the experiments, database with seven microphones was used and MFCC distance was measured to demonstrate the effectiveness of the diagonal loading.

• The Journal of the Acoustical Society of Korea
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• v.38 no.4
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• pp.427-432
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• 2019

In this paper, we propose a home monitoring system using sound event detection based on a bidirectional gated recurrent neural network for the hard-of-hearing. First, in the proposed system, packet loss concealment is used to recover a lost signal captured through wireless sensor networks, and reliable channels are selected using multi-channel cross correlation coefficient for effective sound event detection. The detected sound event is converted into the text and haptic signal through a harmonic/percussive sound source separation method to be provided to hearing impaired people. Experimental results show that the performance of the proposed sound event detection method is superior to the conventional methods and the sound can be expressed into detailed haptic signal using the source separation.

• Proceedings of the Korea Contents Association Conference
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• 2005.05a
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• pp.369-373
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• 2005

This paper proposes a variable step size parameter method in frequency domain ICA (FDICA). The FDICA and the temporal analysis (TA) algorithm are experimented for blind source separation (BSS). This paper will compare the separation qualities of these two algorithms in various reverberation environments. Furthermore, it is shown that the proposed technique has the better separation performance than those of two methods especially in recorded data.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04b
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• pp.547-549
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• 2002

본 논문은 등고선을 이용한 입력된 지표면에 대하여, 소음 영향을 계산하는 프로그램 개발에 관한 것이다. 기존의 등고선 입력 방법을 구현하여 삼차원 지표면 입력을 받으며, 이것이 주위 환경의 일부이다. 삼차원 지표면 입력에 대하여, 국부에 대한 소음 영향을 예측하기 위하여, 지표면을 표현하는 다각형을 작은 삼각형으로 분리되며, 각 작은 삼각형에는 수음자들이 존재한다. 소음 원은 도로, 철도 등 다양하며, 소리가 퍼져나가는 근원이며, 모두 점 음원으로 간주된다. 지표면을 분리된 삼각형에 대하여 기하학 적인 방법을 사용하여 소음 전파 시뮬레이션이 이루어진다. 등고선 입력 방범은 저렴한 삼차원 지표면 입력 방범이며, 사용된 기하학 적인 소음 전파 영향 계산법은 제산 시간을 줄이면서 효율적으로 소음 영향을 예측할수 있게 해 준다.