• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.5
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• pp.853-860
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• 2001

This paper presents the new two denoising methods using wavelet. One is new spatially selective noise filtration(NSSNF) using spatial correlation and the other is undecimated discrete wavelet transform (UDWT) threshold-based. NSSNF got the flexible gain special property of SNR adding new parameter at the existing SSNF and UDWT had superior denosing effect than orthogonal wavelet transform(OWT) applied soft-threshold by applied hard-threshold. We selected additive white gaussian noise(AWGN) in this test environment. Also we analyzed and compared ousting denoising method using SNR as standard of judgement of improvemental effect.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.05a
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• pp.586-591
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• 2002

Recently, the research and discussion to set up the evaluation standard for nor impact noises in multistory residential buildings has been vividly carried out In Korea. Therefore, the correlation between the methods and auditory responses was investigated through this research to investigate the applicability of the L index evaluation method and the reverse A characteristics evaluation method that are listed in JIS A 1419 since Japanese circumstance are similar to Korean after evaluating the duality of Korean multistory residential buildings. As a result, it was found that the correlation between the value resulted from L index evaluation and the value from reverse A characteristics evaluation is high. In addition, it was also revealed that human responses to each Impacter was similar. Consequently, it is considered th:31 the tendency about the two methods would be similar.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.144-146
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• 2020

Discrete Wavelet Transform (DWT) is an effective technique that is commonly used for detecting noise in collected data of an individual sensor. In addition, the detection accuracy can be significant improved by exploiting the correlation in the data of neighboring sensors of Wireless Sensor Networks (WSNs). Principal component analysis is the powerful technique to analyze the correlation in the multivariate data. In this paper, we propose a DWT-PCA combination scheme for noise detection (DWT-PCA-ND). Experimental results on a real dataset show a remarkably higher performance of DWT-PCA-ND comparing to conventional PCA scheme in detection of noise that is a popular anomaly in collected data of WSN.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.04a
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• pp.735-736
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• 2013

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.87-92
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• 2023

With the continuous development of the speech recognition system, the recognition rate for speech has developed rapidly, but it has a disadvantage in that it cannot accurately recognize the voice due to the noise generated by mixing various voices with the noise in the use environment. In order to increase the vocabulary recognition rate when processing speech with environmental noise, noise must be removed. Even in the existing HMM, CHMM, GMM, and DNN applied with AI models, unexpected noise occurs or quantization noise is basically added to the digital signal. When this happens, the source signal is altered or corrupted, which lowers the recognition rate. To solve this problem, each voice In order to efficiently extract the features of the speech signal for the frame, the MFCC was improved and processed. To remove the noise from the speech signal, the noise removal method using the Gaussian model applied noise deviation estimation was improved and applied. The performance evaluation of the proposed model was processed using a cross-correlation coefficient to evaluate the accuracy of speech. As a result of evaluating the recognition rate of the proposed method, it was confirmed that the difference in the average value of the correlation coefficient was improved by 0.53 dB.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.457-458
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• 2013

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.04a
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• pp.159-160
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• 2014

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.04a
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• pp.903-904
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• 2012

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.10a
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• pp.605-606
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• 2012

• The Journal of the Acoustical Society of Korea
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• v.25 no.4E
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• pp.154-158
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• 2006

In this paper, we suggest a noise estimation method for speech enhancement in nonstationary noisy environments. The proposed method consists of the following two main processes. First, in order to receive fewer affect of variable signals, a best fitting regression line is used, which is obtained by applying a least squares method to coefficient magnitudes in a node with a uniform wavelet packet transform. Next, in order to update the noise estimation efficiently, a differential forgetting factor and a correlation coefficient per subband are used, where subband is employed for applying the weighted value according to the change of signals. In particular, this method has the ability to update the noise estimation by using the estimated noise at the previous frame only, without utilizing the statistical information of long past frames and explicit nonspeech frames by voice activity detector. In objective assessments, it was observed that the performance of the proposed method was better than that of the compared (minima controlled recursive averaging, weighted average) methods. Furthermore, the method showed a reliable result even at low SNR.