• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.5
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• pp.222-228
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• 2010

In this paper, we propose a new approach to noise estimation incorporating spectral deviation with soft decision scheme to enhance the intelligibility of the degraded speech signal in non-stationary noisy environments. Since the conventional noise estimation technique based on soft decision scheme estimates and updates the noise power spectrum using a fixed smoothing parameter which was assumed in stationary noisy environments, it is difficult to obtain the robust estimates of noise power spectrum in non-stationary noisy environments that spectral characteristics of noise signal such as restaurant constantly change. In this paper, once we first classify the stationary noise and non-stationary noise environments based on the analysis of spectral deviation of noise signal, we adaptively estimate and update the noise power spectrum according to the classified noise types. The performances of the proposed algorithm are evaluated by ITU-T P. 862 perceptual evaluation of speech quality (PESQ) under various ambient noise environments and show better performances compared with the conventional method.

• Journal of Korea Society of Industrial Information Systems
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• v.11 no.3
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• pp.15-19
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• 2006

In shallow water active sonar environment, reverberation which is a non-white noise is one of the main source of performance degradation of target detection. In this case, the received signal is whitened before applying matched filter known as an optimum filter in the presence of white noise. However implementation of this method is very difficult because of the non-stationary characteristic of reverberation. Traditionally reverberation is assumed local stationary. In this paper, we estimate a range of stationary of reverberation signal, and then propose a pre-whitening method which improve the performance of pre-whitening block normalized matched filter in presence of non-white reverberation noise. Proposed whitener shows better whitening performance than traditional whitener because it use later as well as before reverberation of target signal. To evaluate performance of the proposed whitener, an actual measurement data sampled at the East-Sea is used for computer simulation. The target detector with new whitener is shown better performance than detector with traditional whitener.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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• pp.1009-1012
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• 2004

Recently, the concern on sound qualify (SQ) is on the steep increase in the fields of vehicle and home appliance and over the fast few decades a considerable number of studies have been conducted on SQ evaluation. As a result, basic procedure for SQ evaluation has been already suggested. Although most interesting sounds have time-varying features, however, little is known about their SQ evaluation. The purpose of this study is to systematize a method for SQ evaluation of non-stationary sound. For this, various listening tests procedure for non-stationary sound is introduced and it is attempted to find out correlation between various SQ metrics and subjective data obtain from listening test. Booming of car interior noise in acceleration is used as an example and finally, representative value is obtained for the interesting sensation of non-stationary sound.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11a
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• pp.346.1-346
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• 2002

There have been many studies to visualize the vibration and noise of rotary compressor. Most of these studies assumed that the signal is stationary and the time-averaged signal is used for visualization. However, the noise and vibration signals generated during one cycle of crank shaft vary continuously. In this paper, the noise and vibration of rotary compressor which vary continuously are visualized by short time fourier transform method. (omitted)

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.2
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• pp.239-247
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• 2018

Adaptive Beamforming (ABF) algorithm, which is a typical jammer suppression algorithm, guarantees the performance on the assumption that the jamming characteristics of the TDS (Training Data Sample) are stationary, which are obtained immediately before and after transmitting the pulse signal. Therefore, effective jammer suppression can not be expected when the jamming characteristics are non-stationary. In this paper, we propose a new jammer suppression algorithm, of which power spectrum fluctuates fast. In this case, we assume that the location of the jammer station is fixed during the processing time. By applying the MPM (Matrix Pencil Method) to the jamming signal in TDS, we can estimate jammer parameters such as power and incident angle, of which the power will vary fast in time or range bins after TDS. Though we assume that the jammer station is fixed, the estimated jammer's incident angle has an error due to the noise, which degrades the performance of the jammer suppression as the jammer power increases fast. Therefore, the jammer's incident angle should be re-estimated at each range bin after TDS. By using the re-estimated jammer's incident angle, we can construct new covariance matrix under the non-stationary jamming environment. Then, the optimum weight for the jammer suppression is obtained by inversing matrix estimation method based on the matrix projection with the estimated jammer parameters as variables. To verify the performance of the proposed algorithm, the SINR (signal-to-interference plus noise ratio) loss of the proposed algorithm is compared with that of the conventional ABF algorithm.

• Journal of Korea Multimedia Society
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• v.10 no.8
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• pp.1004-1016
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• 2007

In this paper we proposed a Weight-Update-Control Adaptive Noise Canceller which improves speech when environmental noise is stationary and it is hard to acquire a reference signal. Adaptive Noise Canceller(ANC) needs a reference signal, but it is not easy to measure pure noise without voice for reference in factory. Because there are mixed various mechanical noise and workers' voice. Therefore ANC is not suitable to reduce background noise. So we proposed the method that uses an arbitrary constant as an input signal and inputs microphone signal to the reference signal. The noise is eliminated using updated weights in non-speech range. In speech range the weight is fixed and the modified voice is acquired then voice is restored through transversal filter. The proposed method is based on facts that the factory noise is stationary and the noise is not changed in short conversation range. As a result of simulation using MATLAB, we confirmed that the proposed method is effective for reducing factory noise and has high signal to noise ratio(SNR).

• The Journal of the Acoustical Society of Korea
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• v.32 no.5
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• pp.408-414
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• 2013

The famous filtered-x least mean square (FxLMS) algorithm for active noise control (ANC) systems may become unstable in non-stationary noise environment. To solve this problem, Sun's algorithm and Akhtar's algorithm are developed based on modifying the reference signal in update of FxLMS algorithm, but these two algorithms have dissatisfactory stability in dealing with sustaining impulsive noise. In proposed algorithm, probability estimation and zero-crossing rate (ZCR) control are used to improve the stability and performance, at the same time, an optimal parameter selection based on fuzzy system is utilized. Computer simulation results prove the proposed algorithm has faster convergence and better stability in non-stationary noise environment.

• Proceedings of the KSR Conference
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• 2008.11b
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• pp.1454-1462
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• 2008

This paper is focused on the analysis of the noise and vibration measured near the wheelset of the high-speed trains using a time-varying frequency transform as a preliminary research of running monitoring. Due to the non-stationary characteristics, it is necessary to examine noise and vibration of the train with time-varying frequency transforms. In this paper, the short-time Fourier transform method is utilized - the stored data is localized by modulating with a window function, and Fourier transform is taken to each localized data. For the examination, the non-stationary noise and vibration of the high-speed train's wheelset are measured by using some microphones and accelerometers, and those signals are stored in a on-board data acquisition system. The non-stationary random signal analyses with the short-time Fourier transform are performed, and the result are classified as follows; auto-spectral density, cross-spectral density, frequency response, and coherence functions. From those functions, it is possible to observe the frequency characteristics of sleepers, switchers, tunnels, and steel bridges. Also, some distinct peaks, which are not dependent upon the train's speed, are identified from the results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.9
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• pp.1673-1678
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• 2016

In the area of the speech recognition processing, background noises are caused the incorrect response to the speech input, therefore the speech recognition rates are decreased by the background noises. Accordingly, a more high level noise processing techniques are required since these kinds of noise countermeasures are not simple. Therefore, this paper proposes an algorithm to distinguish between the stationary background noises or non-stationary background noises and the speech signal having short time duration in the noisy environments. The proposed algorithm uses the characteristic parameter of the improved speech signal as an important measure in order to distinguish different types of the background noises and the speech signals. Next, this algorithm estimates various kinds of the background noises using a multi-layer perceptron neural network. In this experiment, it was experimentally clear the estimation of the background noises and the speech signals.

• Journal of Korea Multimedia Society
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• v.12 no.1
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• pp.26-30
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• 2009

We propose an adaptive Recursive Least Rank(RLR) L-filter which uses an L-estimator in order statistics and is based on rank estimate in robust statistics. The proposed RLR L-filter is a non-linear adaptive filter using non-linear adaptive algorithm and adapts itself to optimal filter in the sense of least dispersion measure of errors with non-homogeneous step size. Therefore the filter may be suitable for applications when the transmission channel is nonlinear channels such as Gaussian noise or impulsive noise, or when the signal is non-stationary such as image signal.