• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.4
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• pp.729-736
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• 2019

In this paper, we propose a method to improve the background sound separation performance by adding a Wiener filter to the end of the non - negative matrix factorization method. In the case of a mixed voice signal with background sound, a part that has not yet been completely separated may remain in the signal that separated first by the non-negative matrix factorization method. In this case, it can be reduced in proportion to the size of the residual signal due to the Wiener filter, so that the background sound separation or reduction effect can be expected. Experimental results show that the addition of the Wiener filter is more effective than the case of applying the non-negative matrix factorization method.

• 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 Korean Institute of Communications and Information Sciences
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• v.22 no.12
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• pp.2772-2782
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• 1997

In this paepr, a Wiener filtering method in wavelet domain is proposed for restoring an image corrupted by additive white noise. The proposed method utilizes the characteristics of wavelet transform signals and the local statistics of each subband. When estimating the local statistics in each subband, the size of filter window is varied according to each scale. At this point, the local statistics in each wavelet subband is estimated only by using pixedls which have similar statistical property. Experimental results show that the proposed method has better performance over the conventional Lee filter with a window of fixed size.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.6
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• pp.58-64
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• 2009

Digital radiography (DR) images are corrupted by the additive noise, and also distorted by system impulse response. These unwanted phenomena are obstacles to obtain the desired image. To recover the original image, we applied multiscale Wiener filters in wavelet domain for DR images. The multiscale Wiener filter is first proposed by Chen for the restoration of fractal signals which are distorted by the system impulse response and additive noise. In this paper, we extended the multiscale Wiener filter to the two dimensional data. To compare the performance of ours with others, some simulations are given for a couple of wavelet filters with different wavelet levels, system impulse reponses and various noise power. When the addive noise powers are between 20-32 dB, the signal to noise ratio(SNR) of the proposed system is 0.5-2.0 dB better than that of the traditional Wiener filter method.

• The Journal of the Acoustical Society of Korea
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• v.38 no.1
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• pp.39-46
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• 2019

In this paper, combining features is proposed as a way to enhance the classification accuracy of sounds under noisy environments using the CNN (Convolutional Neural Network) structure. A robust log Mel-filter bank using Wiener filter and PNCCs (Power Normalized Cepstral Coefficients) are extracted to form a 2-dimensional feature that is used as input to the CNN structure. An ebird database is used to classify 43 types of bird species in their natural environment. To evaluate the performance of the combined features under noisy environments, the database is augmented with 3 types of noise under 4 different SNRs (Signal to Noise Ratios) (20 dB, 10 dB, 5 dB, 0 dB). The combined feature is compared to the log Mel-filter bank with and without incorporating the Wiener filter and the PNCCs. The combined feature is shown to outperform the other mentioned features under clean environments with a 1.34 % increase in overall average accuracy. Additionally, the accuracy under noisy environments at the 4 SNR levels is increased by 1.06 % and 0.65 % for shop and schoolyard noise backgrounds, respectively.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.519-522
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• 2000

Digital watermarking has been proposed as a solution to the problem of copyright protection of the multimedia documents. In this paper a new watermarking method for digital images operating in the frequency domain is proposed. In our approach, DCT coefficients of the watermark are added to the low frequency region of the host image, and extract it using the Wiener Filter. Due to the characteristic of the wiener filtering, the watermark is robust to various image processing techniques. Experimental results show that it is possible to reliably extract the watermark without degrading image quality.

• The Journal of Engineering Research
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• v.3 no.1
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• pp.43-46
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• 1998

Considerable research in the last three decades has examined the problem of enhancement of speech degraded by additive background noise. We compare traditional methods such as spectral subtraction and Wiener filter, recently proposed psychoacoustic model based methods such as perceptual filter and noise suppression in EVRC in terms of performance and complexity.

• Journal of Korea Multimedia Society
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• v.4 no.1
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• pp.46-55
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• 2001

In this paper, a new voice activity detection method estimating SNR of enhanced speech with extended spectral subtraction (ESS) is proposed. Voice activity detection is performed by putting an second Wiener filter behind an Wiener filter used in the ESS to estimate speech and noise power of output signal of first Wiener filter. The proposed voice activity detection method does not require many computational loads and performs well under severe input SNR. Boll's spectral substraction algorithm with proposed voice activity detection was compared to ESS under several noise environment having different time-frequency distributions. During speech and non-speech activity, performance of Boll's spectral substraction algorithm with proposed voice activity detection is superior to that of ESS.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.2
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• pp.208-213
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• 2010

Generally, the mal factor for speech recognition is the background noise in speech recognition. The noise is the reason to reduce the speech recognition performance. Owing to the fact, the place to recognize is very important. To improve the recognition performance from the sound having noise, we implemented the noise filtered Wiener filter at the signal process step which adopted the FIR filter. In FIR filter, it deal with the filtered speech signal which is appropriate frequency range of human speech frequency range. Therefore, we make the recognition system distinguish between noise and speech sound from the incoming speech signal.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.113-122
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• 2016

In this paper, we suggest a approach which comprises fast Fourier transform inversion by wavelet noise attenuation. It represents an inverse filtering by adopting a factor into the Wiener filtering, and the optimal factor is chosen to minimize the overall mean squared error. in order to apply the Wiener filter, we have to compute the power spectrum of original image from the corrupted figure. Since the Wiener filtering contains the inverse filtering process, it expands the noise when the blurring filter is not invertible. To remove the large noises, the best is to remove the noise using wavelet threshold. Wavelet noise attenuation steps are consisted of inverse filtering and noise reduction by Wavelet functions. experimental results have not outperformed the other methods over the overall restoration performance.