• Journal of Korea Multimedia Society
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• v.19 no.7
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• pp.1127-1136
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• 2016

Speech enhancement algorithm plays an important role in numerous speech signal processing applications. Over the last few decades, many algorithms have been studied for speech enhancement. The algorithms are based on spectral subtraction, Wiener filter, and subspace method etc. They have good performance of speech enhancement, but the performance can be deteriorated in specific noises or low SNR environment. In this paper, a new speech enhancement algorithms are proposed based on adaptive noise canceller. And the proposed algorithm improved performance of adaptive noise cancelling using 2-D binary mask. From objective experimental index, it is confirmed that the proposed algorithm is useful and has better performance than recently proposed speech enhancement algorithms.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.9
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• pp.165-170
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• 2014

Speech enhancement techniques that remove surrounding noise are stressed to preprocessor of speech recognition. Among the various speech enhancement techniques, Codebook-based Speech Enhancement (CBSE) operates efficiently in non-stationary noise environments. But, CBSE has some problems that inaccurate gains can be estimated if mismatch occur between input noisy signal and trained speech/noise codevectors. In this paper, the Normalized Weighting Factor (NWF) is calculated by long-term noise estimation algorithm based on Signal-to-Noise Ratio, compensated to the conventional inaccurate gains. The proposed CBSE shows better performance than conventional CBSE.

• The Journal of the Acoustical Society of Korea
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• v.29 no.2E
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• pp.86-99
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• 2010

This paper analyzes the performance of various single channel speech enhancement algorithms when they are applied to automatic speech recognition (ASR) systems as a preprocessor. The functional modules of speech enhancement systems are first divided into four major modules such as a gain estimator, a noise power spectrum estimator, a priori signal to noise ratio (SNR) estimator, and a speech absence probability (SAP) estimator. We investigate the relationship between speech recognition accuracy and the roles of each module. Simulation results show that the Wiener filter outperforms other gain functions such as minimum mean square error-short time spectral amplitude (MMSE-STSA) and minimum mean square error-log spectral amplitude (MMSE-LSA) estimators when a perfect noise estimator is applied. When the performance of the noise estimator degrades, however, MMSE methods including the decision directed module to estimate a priori SNR and the SAP estimation module helps to improve the performance of the enhancement algorithm for speech recognition systems.

• ETRI Journal
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• v.38 no.2
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• pp.366-375
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• 2016

This paper describes an algorithm to suppress composite noise in a two-microphone speech enhancement system for robust hands-free speech communication. The proposed algorithm has four stages. The first stage estimates the power spectral density of the residual stationary noise, which is based on the detection of nonstationary signal-dominant time-frequency bins (TFBs) at the generalized sidelobe canceller output. Second, speech-dominant TFBs are identified among the previously detected nonstationary signal-dominant TFBs, and power spectral densities of speech and residual nonstationary noise are estimated. In the final stage, the bin-wise output signal-to-noise ratio is obtained with these power estimates and a Wiener post-filter is constructed to attenuate the residual noise. Compared to the conventional beamforming and post-filter algorithms, the proposed speech enhancement algorithm shows significant performance improvement in terms of perceptual evaluation of speech quality.

• The Journal of the Acoustical Society of Korea
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• v.22 no.3E
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• pp.110-112
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• 2003

There have been numerous studies on the enhancement of the noisy speech signal. In this paper, we propose a completely new speech enhancement scheme, that is, a filtering of a dissonant frequency (especially F# in each octave of the tempered scale) based on the fundamental frequency which is developed in frequency domain. In order to evaluate the performance of the proposed enhancement scheme, subjective tests (MOS tests) were conducted. The subjective test results indicate that the proposed method provides a significant gain in audible improvement especially for speech contaminated by colored noise and speaking in a husky voice. Therefore when the filter is employed as a pre-filter for speech enhancement, the output speech quality and intelligibility is greatly enhanced.

• The Journal of the Acoustical Society of Korea
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• v.22 no.4E
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• pp.161-166
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• 2003

We present an adaptive wavelet packet based speech enhancement method with robust voice activity detection (VAD) in non-stationary noise environment. The proposed method can be divided into two main procedures. The first procedure is a VAD with adaptive wavelet packet transform. And the other is a speech enhancement procedure based on the proposed VAD method. The proposed VAD method shows remarkable performance even in low SNRs and non-stationary noise environment. And subjective evaluation shows that the performance of the proposed speech enhancement method with wavelet bases is better than that with Fourier basis.

• Journal of Korea Multimedia Society
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• v.22 no.7
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• pp.749-758
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• 2019

This paper proposes a data augmentation algorithm to improve the performance of DNN(Deep Neural Network) based speech enhancement. Many deep learning models are exploring algorithms to maximize the performance in limited amount of data. The most commonly used algorithm is the data augmentation which is the technique artificially increases the amount of data. For the effective data augmentation algorithm, we used a formant enhancement method that assign the different weights to the formant frequencies. The DNN model which is trained using the proposed data augmentation algorithm was evaluated in various noise environments. The speech enhancement performance of the DNN model with the proposed data augmentation algorithm was compared with the algorithms which are the DNN model with the conventional data augmentation and without the data augmentation. As a result, the proposed data augmentation algorithm showed the higher speech enhancement performance than the other algorithms.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.784-788
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• 2008

In this research, we propose a remote speech input unit, a new method of user-friendly speech input in speech recognition system. We focused the user friendliness on hands-free and microphone independence in speech recognition applications. Our module adopts two algorithms, the automatic speech detection and speech enhancement based on the microphone array-based beamforming method. In the performance evaluation of speech detection, within-200msec accuracy with respect to the manually detected positions is about 97percent under the noise environments of 25dB of the SNR. The microphone array-based speech enhancement using the delay-and-sum beamforming algorithm shows about 6dB of maximum SNR gain over a single microphone and more than 12% of error reduction rate in speech recognition.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.6
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• pp.118-123
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• 2007

In this paper, we propose an improved global soft decision for speech enhancement in noise environments. From an examination of statistical model-based speech enhancement, it is shown that the global soft decision has a fundamental drawback at the offset region of speech signals. To overcome the drawback, we apply a new speech enhancement method based on a smoothed Global likelihood ratio to the global soft decision. Performances of the proposed method are evaluated by subjective tests under various environments and yield better results compared with the reported speech enhancement method.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.10
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• pp.35-41
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• 1993

In this paper, a new speech enhancement method for speech signal corrupted by environmental noise is proposed. Two signals are obtained from the microphone and from the accelerometer attached to the neck, respectively. Since two signals are generated from same source signal, both signals are closely correlated. And environmental noise has no effect on the accelerometer signal. The speech enhancement system identifies the optimum linear system between two signals on the basis of the dependence between the signals. The enhanced speech can be obtained by filtering the noise-free accelerometer signal. Since the characteristcs of the speech signal and environmental noise are changing with time, adaptive filtering system has to be used for characterizing the time-varing system. Simulation results show 7dB enhancement with 0dB speech signal level relative to the white noise.