• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1995.10a
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• pp.86-91
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• 1995

음원의 위치를 찾는 문제에 있어서 비독립 음원들이 존재하는 경우, 근접장에서는 음원으로부터 나오는 파형을 평면파로 가정할 수 없고 구면파로 가정해야 하며, 여러개의 비독립 음원이 존재한다고 가정하여 각각의 음원의 위치를 변화시켜 가면서 MUSIC파워를 계산하여야 한다. 실제 음원의 갯수보다 가정한 음원의 갯수가 작으면 정확한 음원의 위치를 가르키지 못하며 MUSIC 파워 값도 작다. 실제 음원의 갯수와 가정한 음원의 갯수가 같으면 정확한 음원의 위치를 가르치며 MUSIC 파워 값이 크게 얻어진다. 실제 음원의 갯수보다 가정한 음원의 갯수가 큰 경우 실제의 음원의 위치를 포함하여 다른 음원의 위치를 얻을 수 있으나 그 음원의 세기는 무시할 정도로 작다. 즉 실제 음원의 갯수보다 많은 수의 음원을 가정하여 음원을 탐지할 수 있다. 일반 음장 즉 독립 음원과 비독립 음원이 공존하는 경우 실제의 음원의 갯수만큼 가정한다면 음원이 위치를 찾을 수 있다.

• Journal of KSNVE
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• v.6 no.6
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• pp.835-842
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• 1996

To identify the locations and strengths of acoustic sources, one may use a microphone line array. Apparent advantage of the source identification method utilizing a line array is that it requires less measurement points than intensity method and holography. This method is based on the information of magnitude and phase difference between pressure signals at each microphone. Since those differences are dependent on the source model, we have to assume them such as plane, monopole, etc. In this paper the conventional source identification methods such as beamforming method and MUSIC method are briefly reviewed by modeling a source as plane and spherical wave, then a modified method is introduced. This can be applied to sound field which may by either coherent or incoherent. Typical simulations and experiment are performed to confirm this identification method.

• 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 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.

• Journal of Broadcast Engineering
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• v.21 no.2
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• pp.169-179
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• 2016

Recently, MPEG-H has been standardizing for a multimedia coder in UHDTV (Ultra-High-Definition TV). Thus, the demand for not only channel-based audio contents but also object-based audio contents is more increasing, which results in developing a new technique of converting channel-based audio contents to object-based ones. In this paper, a non-uniform linear microphone array based source separation method is proposed for realizing such conversion. The proposed method first analyzes the arrival time differences of input audio sources to each of the microphones, and the spectral magnitudes of each sound source are estimated at the horizontal directions based on the analyzed time differences. In order to demonstrate the effectiveness of the proposed method, objective performance measures of the proposed method are compared with those of conventional methods such as an MVDR (Minimum Variance Distortionless Response) beamformer and an ICA (Independent Component Analysis) method. As a result, it is shown that the proposed separation method has better separation performance than the conventional separation methods.

• The Journal of the Acoustical Society of Korea
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• v.40 no.5
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• pp.503-514
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• 2021

Reverberation in speech signals tends to significantly degrade the performance of the Blind Source Separation (BSS) system. Especially in online systems, the performance degradation becomes severe. Methods based on joint diagonalizability constraints have been recently developed to tackle the problem. To improve the quality of separated speech, in this paper, we add the proposed de-reverberation method to the online BSS algorithm based on the constraints in reverberant environments. Through experiments on the WSJCAM0 corpus, the proposed method was compared with the existing online BSS algorithm. The performance evaluation by the Signal-to-Distortion Ratio and the Perceptual Evaluation of Speech Quality demonstrated that SDR improved from 1.23 dB to 3.76 dB and PESQ improved from 1.15 to 2.12 on average.

• Journal of KIISE:Software and Applications
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• v.36 no.3
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• pp.179-186
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• 2009

This paper proposes a target speech extraction which restores speech signal of a target speaker form noisy convolutive mixture of speech and an interference source. We assume that the target speaker is known and his/her utterances are available in the training time. Incorporating the additional information extracted from the training utterances into the separation, we combine convolutive blind source separation(CBSS) and non-negative decomposition techniques, e.g., probabilistic latent variable model. The nonnegative decomposition is used to learn a set of bases from the spectrogram of the training utterances, where the bases represent the spectral information corresponding to the target speaker. Based on the learned spectral bases, our method provides two postprocessing steps for CBSS. Channel selection step finds a desirable output channel from CBSS, which dominantly contains the target speech. Reconstruct step recovers the original spectrogram of the target speech from the selected output channel so that the remained interference source and background noise are suppressed. Experimental results show that our method substantially improves the separation results of CBSS and, as a result, successfully recovers the target speech.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.06e
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• pp.125-128
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• 1998

최근의 연구에서 해양의 내부파가 음파의 전달에 영향을 주어 비정상적인 손실을 일으키는 것으로 밝혀졌다. 일련의 실험을 통하여 한국 동해에도 강한 수온약층을 중심으로 한 내부파가 존재하는 것으로 밝혀졌으며, 음원과 수신기를 이용한 실험을 통해서도 관측된 내부파의 주기에 해당하는 음파의 변동 특성이 확인되었다. 내부파가 음파의 전파에 영향을 미치는 것은 모드간 간섭을 통하여 이루어진다. 본 논문에서는 모드간섭의 이론적 설명과 함께 음향모델을 통하여 내부파의 영향을 추정하였다. 모델링 결과 내부파는 음파의 모드간 에너지 전이를 일으켜서 에너지를 산란시키는 효과가 있는 것으로 보인다. 한편 거리독립 환경과 내부파가 존재하는 환경간에는 주파수 1 kHz를 기준으로 하여 거리에 따라 약 10dB까지의 전파손실 차이를 나타낸다.

• The Journal of the Acoustical Society of Korea
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• v.41 no.3
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• pp.326-334
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• 2022

In this paper, we propose speech enhancement algorithm as a pre-processing for robust speech recognition in noisy environments. Auxiliary-function-based Independent Vector Analysis (AuxIVA) is performed with weighted covariance matrix using time-varying variances with scaling factor from target masks representing time-frequency contributions of target speech. The mask estimates can be obtained using Neural Network (NN) pre-trained for speech extraction or diffuseness using Coherence-to-Diffuse power Ratio (CDR) to find the direct sounds component of a target speech. In addition, outputs for omni-directional noise are closely chained by sharing the time-varying variances similarly to independent subspace analysis or IVA. The speech extraction method based on AuxIVA is also performed in Independent Low-Rank Matrix Analysis (ILRMA) framework by extending the Non-negative Matrix Factorization (NMF) for noise outputs to Non-negative Tensor Factorization (NTF) to maintain the inter-channel dependency in noise output channels. Experimental results on the CHiME-4 datasets demonstrate the effectiveness of the presented algorithms.

• The Journal of the Korea Contents Association
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• v.6 no.12
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• pp.89-95
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• 2006

The performance of blind source separation(BSS) using independent component analysis (ICA) declines significantly in a reverberant environment. A post-processing method proposed in this paper was designed to remove the residual component precisely. The proposed method used modified NLMS(normalized least mean square) filter in frequency domain, to estimate cross-talk path that causes residual cross-talk components. Residual cross-talk components in one channel is correspond to direct components in another channel. Therefore, we can estimate cross-talk path using another channel input signals from adaptive filter. Step size is normalized by input signal power in conventional NLMS filter, but it is normalized by sum of input signal power and error signal power in modified NLMS filter. By using this method, we can prevent misadjustment of filter weights. The estimated residual cross-talk components are subtracted by non-stationary spectral subtraction. The computer simulation results using speech signals show that the proposed method improves the noise reduction ratio(NRR) by approximately 3dB on conventional FDICA.