• The Journal of the Acoustical Society of Korea
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• v.16 no.6
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• pp.94-99
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• 1997

This paper describes a technique of probabilistic spectral subtraction which uses the knowledge of both noise and speech so as to reduce automatic speech recognition errors in noisy environments. Spectral subtraction method estimates a noise prototype in non-speech intervals and the spectrum of clean speech is obtained from the spectrum of noisy speech by subtracting this noise prototype. Thus noise can not be suppressed effectively using a single noise prototype in case the characteristics of the noise prototype are different from those of the noise contained in input noisy speech. To modify such a drawback, multiple noise prototypes are used in probabilistic subtraction method. In this paper, the probabilistic characteristics of noise and the knowledge of speech which is embedded in hidden Markov models trained in clean environments are used to suppress noise. Futhermore, dynamic feature parameters are considered as well as static feature parameters for effective noise suppression. The proposed method reduced error rates in the recognition of 50 Korean words. The recognition rate was 86.25% with the probabilistic subtraction, 72.75% without any noise suppression method and 80.25% with spectral subtraction at SNR(Signal-to-Noise Ratio) 10 dB.

• The Magazine of the Society of Air-Conditioning and Refrigerating Engineers of Korea
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• v.26 no.6
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• pp.472-480
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• 1997

• Journal of electromagnetic engineering and science
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• v.16 no.3
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• pp.191-193
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• 2016

This paper proposes a voltage-controlled oscillator (VCO) that has broadband turning and low-level of phase noise characteristics. Due to the micro-strip line resonant circuit with a low Q value, which is applied to the broadband tuning range, the depreciated phase noise performance is compensated by restraining the harmonics of the oscillating frequency. The VCO was designed according to the proposed structure as well as the conventional structure, and the superiority of the proposed structure was verified through its simulation, fabrication, and measurement.

• 한국ITS학회:학술대회논문집
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• v.2007 no.10
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• pp.334-337
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• 2007

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2010.10a
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• pp.212-213
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• 2010

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.04a
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• pp.211-212
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• 2014

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2010.05a
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• pp.521-522
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• 2010

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.3
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• pp.465-470
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• 2021

A robust speech recognition technology is required that does not degrade the performance of speech recognition and the quality of the speech when speech recognition is performed in an actual environment of the speech mixed with noise. With the development of such speech recognition technology, it is necessary to develop an application that achieves stable and high speech recognition rate even in a noisy environment similar to the human speech spectrum. Therefore, this paper proposes a speech enhancement algorithm that processes a noise suppression based on the MMSA-STSA estimation algorithm, which is a short-time spectral amplitude method based on the error of the least mean square. This algorithm is an effective nonlinear speech enhancement algorithm based on a single channel input and has high noise suppression performance. Moreover this algorithm is a technique that reduces the amount of distortion of the speech based on the statistical model of the speech. In this experiment, in order to verify the effectiveness of the MMSA-STSA estimation algorithm, the effectiveness of the proposed algorithm is verified by comparing the input speech waveform and the output speech waveform.

• Geophysics and Geophysical Exploration
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• v.26 no.2
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• pp.37-51
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• 2023

The ground roll is the most common coherent noise in land seismic data and has an amplitude much larger than the reflection event we usually want to obtain. Therefore, ground roll suppression is a crucial step in seismic data processing. Several techniques, such as f-k filtering and curvelet transform, have been developed to suppress the ground roll. However, the existing methods still require improvements in suppression performance and efficiency. Various studies on the suppression of ground roll in seismic data have recently been conducted using deep learning methods developed for image processing. In this paper, we introduce three models (DnCNN (De-noiseCNN), pix2pix, and CycleGAN), based on convolutional neural network (CNN) or conditional generative adversarial network (cGAN), for ground roll suppression and explain them in detail through numerical examples. Common shot gathers from the same field were divided into training and test datasets to compare the algorithms. We trained the models using the training data and evaluated their performances using the test data. When training these models with field data, ground roll removed data are required; therefore, the ground roll is suppressed by f-k filtering and used as the ground-truth data. To evaluate the performance of the deep learning models and compare the training results, we utilized quantitative indicators such as the correlation coefficient and structural similarity index measure (SSIM) based on the similarity to the ground-truth data. The DnCNN model exhibited the best performance, and we confirmed that other models could also be applied to suppress the ground roll.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.5
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• pp.23-32
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• 2015

The advanced receiver which can manage inter-cell interference is required to cope with the explosively increasing mobile data traffic. 3rd Generation Partnership Project (3GPP) has discussed network assisted interference cancellation and suppression (NAICS) to improve signal-to-noise-plus-interference ratio (SINR) and receiver performance by suppression or cancellation of interference signal from inter-cells. In this paper, we propose the novel hybrid receiver Full Suppression Cancellation (FSC) to reduce the interference from neighbor cell in backhaul system. The proposed receiver can suppress and cancel the interference by combining Interference Rejection Combining (IRC) with Successive Interference Cancellation (SIC). We perform the system level simulation based on 20MHz bandwidth of 3GPP LTE-Advanced system. Simulation results show that the proposed receiver can improve error rate and throughput of conventional system.