• 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.39 no.5
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• pp.490-496
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• 2020

We present a method to improve the performance of the multi-channel Wiener filter in noisy environment. To build subspace-based multi-channel Wiener filter, in the case of single target source, the target speech component can be effectively estimated in the principal subspace of speech correlation matrix. The speech correlation matrix can be estimated by subtracting noise correlation matrix from signal correlation matrix based on the assumption that the cross-correlation between speech and interfering noise is negligible compared with speech correlation. However, this assumption is not valid in the presence of strong interfering noise and significant error can be induced in the principal subspace accordingly. In this paper, we propose to adjust the principal subspace vector using speech presence probability and the steering vector for the desired speech source. The multi-channel speech presence probability is derived in the principal subspace and applied to adjust the principal subspace vector. Simulation results show that the proposed method improves the performance of multi-channel Wiener filter in noisy environment.

• Journal of Broadcast Engineering
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• v.18 no.3
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• pp.455-462
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• 2013

This paper introduces the Spatially Preprocessed Speech Distortion Weighted Multi-channel Wiener Filter (SP-SDW-MWF) for multi-microphone noise reduction and proposes a method to determine the speech distortion weights. The SP-SDW-MWF is known as a robust noise reduction algorithm against the error caused by the mismatch in microphones. The SP-SDW-MWF adopts weights which determine the amount of noise reduction at the expense of introducing speech distortion in the noise-suppressed speech. In this paper, we use the error of power spectral density between the estimated signal and the desired signal as the evaluation measure. Thus the a priori SNR is used to control the speech distortion weights in the frequency domain. In the experimental results, the proposed method yields better result in terms of MFCC distortion compared to the conventional method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.5C
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• pp.302-308
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• 2011

In this paper we propose a coordinated beamforming(CBF) scheme considering the effects of feedback quantization and delay in time-varying multiple-input multiple-output(MIMO) broadcast channels. By equal power allocation per data stream, the proposed CBF scheme transmits multiple data streams per user terminals without additional feedback overhead when quantized feedback information is used. The proposed CBF scheme also applies a linear channel predictor to each user terminals to prevent errors due to feedback delays that are not evitable in practical wireless systems. Each user terminal utilizes Wiener filter to predict future channel responses and generates feedback information based on the predicted channels. Consequently the proposed CBF scheme adapting Wiener filter improves system performances compared with the conventional scheme using delayed feedback.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.457-459
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• 2018

화자인식 기술은 주어진 임의의 두 발화로부터 발화자의 일치 여부를 판단하여 등록된 화자의 목록으로부터 임의로 입력된 발화의 발화자를 식별하는 기술이다. 그러나, 배경잡음이나 반향이 존재하는 경우에는 음성신호가 왜곡되어 화자인식 성능이 저하될 수 있기 때문에 별도의 음성신호 전처리 알고리즘을 함께 사용할 수 있다. 본 논문에서는 배경잡음이 존재하는 환경에서 다수의 마이크로폰을 통해 수집한 음성신호에 대해 화자인식을 수행하는 방법으로써 parametric multi-channel Wiener filter (PMWF)를 이용한 화자일치 점수 앙상블 기법을 제안한다. 입력신호의 신호대잡음비를 기준으로 점수 결합 시 사용되는 결합계수를 정하고, Wiener filter 로 잡음을 제거하여 얻은 점수와 minimum variance distortionless response (MVDR) 빔포머를 통해 잡음을 제거하여 얻은 정수를 가중결합하는 방식으로 동일오류율을 측정한 결과, 각 전처리 알고리즘을 독립적으로 사용하여 점수를 계산한 경우보다 우수한 성능을 보임을 확인할 수 있었다.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2001.05a
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• pp.280-287
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• 2001

The studies to estimate the surface spectral reflectance of an object have received widespread attention using the multi-spectral camera system. However, the multi-spectral camera system requires the additional color filter according to increment of the channel and system complexity is increased by multiple capture. Thus, this paper proposes an algorithm to reduce the estimation error of surface spectral reflectance with the conventional 3-band RGB camera. In the proposed method, adaptive principal components for each pixel are calculated by renewing the population of surface reflectances and the adaptive principal components can reduce estimation error of surface spectral reflectance of current pixel. To evacuate performance of the proposed estimation method, 3-band principal component analysis, 5-band wiener estimation method, and the proposed method are compared in the estimation experiment with the Macbeth ColorChecker. As a result, the proposed method showed a lower mean square ems between the estimated and the measured spectra compared to the conventional 3-band principal component analysis method and represented a similar or advanced estimation performance compared to the 5-band wiener method.

• The Journal of the Acoustical Society of Korea
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• v.23 no.2E
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• pp.56-67
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• 2004

Despite abundant research outcomes of blind source separation (BSS) in many types of simulated environments, their performances are still not satisfactory to be applied to the real environments. The major obstacle may seem the finite filter length of the assumed mixing model and the nonlinear sensor noises. This paper presents a two-step speech enhancement method with multiple microphone inputs. The first step performs a frequency-domain BSS algorithm to produce multiple outputs without any prior knowledge of the mixed source signals. The second step further removes the remaining cross-channel interference by a spectral cancellation approach using a probabilistic source absence/presence detection technique. The desired primary source is detected every frame of the signal, and the secondary source is estimated in the power spectral domain using the other BSS output as a reference interfering source. Then the estimated secondary source is subtracted to reduce the cross-channel interference. Our experimental results show good separation enhancement performances on the real recordings of speech and music signals compared to the conventional BSS methods.

• Journal of Korea Multimedia Society
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• v.4 no.1
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• pp.37-45
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• 2001

The studies to estimate the surface spectral reflectance of an object have received widespread attention using the multi-spectral camera system. However, the multi-spectral camera system requires the additional color filter according to increment of the channel and system complexity is increased by multiple capture. Thus, this paper proposes an algorithm to reduce the estimation error of surface spectral reflectance with the conventional 3-band RGB camera. In the proposed method, adaptive principal components for each pixel are calculated by renewing the population of surface reflectances and the adaptive principal components can reduce estimation error of surface spectral reflectance of current pixel. To evaluate performance of the proposed estimation method, 3-band principal component analysis, 5-band wiener estimation method, and the proposed method are compared in the estimation experiment with the Macbeth Color Checker. As a result, the proposed method showed a lower mean square error between the estimated and the measured spectra compared to the conventional 3-band principal component analysis method and represented a similar or advanced estimation performance compared to the 5-band wiener method.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.934-936
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• 2017

화자인식 기술은 등록된 화자 목록 내 화자 또는 사칭 화자의 발화로부터 발화자를 식별하는 기술로써, 음성 소스를 기반으로 동작하는 디바이스의 개인화를 위해 필요한 기술이다. 본 논문에서는 차량 잡음이 존재하는 자동차 주행 환경을 타겟으로 하는 화자인식 시스템 개발 방법을 제안한다. 차량 잡음에 의해 오염된 음성신호로부터 잡음 성분을 제거하기 위해 parametric multi-channel Wiener filter (PWMF)를 이용하여 실험한 결과, 남성화자 조건에서는 PMWF의 내부 파라미터 조절을 통해 필터를 minimum variance distortionless response (MVDR) 빔포머로 동작하도록 설정하였을 때, 여성화자 조건에서는 잡음을 제거하지 않았을 때 가장 낮은 동일오류율을 보임을 확인할 수 있었다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.1
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• pp.32-40
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• 2013

In this paper, we propose a multi-channel speech enhancement algorithm using beamforming and direction-of-arrival (DOA)-based masking. The proposed algorithm enhances noisy speech basically by the linearly constrained minimum variance (LCMV) algorithm and then a mel-scale Wiener filter designed using DOA-based masking is applied to remove still remaining noises. To improve the performance, we optimize the learning rate of the adaptive filters in LCMV and the DOA threshold to detect target speech spectrum. As performance indices, the perceptual evaluation of speech quality (PESQ) score and output SNRs are measured. Experimantal results show that the proposed algorithm outperforms the conventional LCMV beamformer by 0.09 in PESQ score and 5.75 dB in output SNR, respectively.