• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.663-668
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• 2008

In this paper, we analyzed the error rate Performance of convolution coded 16 QAM signal in impulsive noise Environments. We used convolution rode and maximum ratio combining diversity for performance improvement. We analyzed the error rate performance of 16 QAM signal in implusive noise environments compared with gaussian noise environments. As a result of analysis, there is a BER segment where the efficiency of system does not improve until which limit to raise a signal power potential from impulsive noise environment when the signal power potential which goes over this limit is supplied, BER efficiency improve much more.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.1
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• pp.86-91
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• 2012

To provide high voice quality of real-time voice communication based on telematics exposed to various noise environments, the noise reduction algorithm with low computing load is required to effectively remove the noise. In this paper, we propose a noise reduction algorithm based on Mel-Filter and illustrate the proposed algorithm comparing with conventional noise reduction algorithms. As a experimental result that evaluates the performance of the noise reduction algorithms under the car and babble noise environments, the proposed noise reduction algorithm has the lower computing load with the similar PESQ score compared to the conventional noise reduction algorithms. It proves that the proposed noise reduction algorithm can efficiently remove the noise in mobile telematics.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.4
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• pp.181-186
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• 2011

In this paper, we propose the Noise Cancel DTW that to use a kind of feature compensation. This method is not to use estimated noise but we use real life environment noise data for Voice Recognition. And we applied this contaminated data for recognition reference model that suitable for noise environment. NCDTW is combined with surround noise when generating reference patten. We improved voice recognition rate at mobile environment to use NCDTW.

• The Journal of the Korea Contents Association
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• v.9 no.1
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• pp.139-146
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• 2009

This paper addresses a modified spectral subtraction schemes which is suitable to speech recognition under low signal-to-noise ratio (SNR) noisy environment such as the automatic speech recognition (ASR) system in car. The conventional spectral subtraction schemes rely on the SNR such that attenuation is imposed on that part of the spectrum that appears to have low SNR, and accentuation is made on that part of high SNR. However, such postulation is adequate for high SNR environment, it is grossly inadequate for low SNR scenarios such as that of car environment. Proposed methods focused specifically to low SNR noisy environment by using weighting function for enhancing speech dominant region in speech spectrum. Experimental results by using voice commands for car show the superior performance of the proposed method over conventional methods.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.6
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• pp.48-59
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• 2000

Eigendecomposition based direction-of-arrival(DOA) estimation algorithm such as MUSIC(multiple signal classification) is known to perform well and provide high resolution in white noise environment. However, its performance degrades severely when the noise process is not white. In this paper we consider the DOA estimation problem in a colored noise environment as a problem of extracting periodic signals from noise, and we take the problem to the wavelet domain. Covariance matrix of multiscale components which are obtained by taking wavelet decomposition on the noise has a special structure which can be approximated with a banded sparse matrix. Compared with noise the correlation between multiscale components of narrowband signal decays slowly, hence the covariance matrix does not have a banded structure. Based on this fact we propose a DOA estimation algorithm that transforms the covariance matrix into wavelet domain and removes noise components located in specific bands. Simulations have been carried out to analyze the proposed algorithm in colored noise processes with various correlation properties.

• The Journal of the Acoustical Society of Korea
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• v.29 no.7
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• pp.468-474
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• 2010

The major factor that disturbs practical use of speech recognition is distortion by the ambient and channel noises. Generally, the ambient noise drops the performance and restricts places to use. DSR (Distributed Speech Recognition) based speech recognition also has this problem. Various noise cancelling algorithms are applied to solve this problem, but loss of spectrum and remaining noise by incorrect noise estimation at low SNR environments cause drop of recognition rate. This paper proposes methods for speech enhancement. This method uses MMSE-STSA for noise cancelling and ideal binary mask to compensate damaged spectrum. According to experiments at noisy environment (SNR 15 dB ~ 0 dB), the proposed methods showed better spectral results and recognition performance.

• The Journal of the Acoustical Society of Korea
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• v.27 no.7
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• pp.379-385
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• 2008

In this paper, we propose an effective speech reinforcement technique under the near-end and the far-end noise environments. In general, since the intelligibility of the far-end speech for the near-end listener is significantly reduced under near-end noise environments, we require a far-end speech reinforcement approach to avoid this phenomena. Specifically, based on the estimated background noise spectrum of the near-end, we reinforce the far-end speech spectrum by incorporating the more general cases under the near-end with background noise. Also, we propose the novel approach to reinforce the actual speech signal except for the noise signal in the far-end noisy speech signal. The performance of the proposed algorithm is evaluated by the CCR (Comparison Category Rating) test of the method for subjective determination of transmission quality in ITU-T P.800 under various noise environments and shows better performances compared with the conventional 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.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.5 no.2 s.10
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• pp.1-10
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• 2006

In this paper, VTS(Vector Taylor Series) algorithm, which was proposed by Moreno at Carnegie Mellon University in 1996, is analyzed and simulated. VTS is considered to be one of the robust speech recognition techniques where model parameter conversion technique is adapted. To evaluation performance of the VTS algorithm, We used CMN(Cepstral Mean Normalization) technique which is one of the well-known noise processing methods. And the recognition rate is evaluated when white gaussian and street noise are employed as background noise. Also, the simulation result is analyzed in order to be compared with the previous one which was performed by Moreno.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.421-424
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• 2008

This paper proposes an adaptive noise suppression system based on human auditory model to enhance speech signal that is degraded by various background noises. The proposed system detects voiced and unvoiced sections for each frame and implements the adaptive auditory process, then reduces the noise speech signal using neural network including amplitude component and phase component. Base on measuring signal-to-noise ratios, experiments confirm that the proposed system is effective for speech signal that is degraded by various noises.