Sung-Il Yang

Hanyang University, Ansan, Gyeonggi, South Korea

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Publications (9)7.17 Total impact

  • Sung-il JUNG, Younghun KWON, Sung-il YANG
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    ABSTRACT: A speech enhancement method is proposed that can be implemented efficiently due to its use of wavelet packet transform. The proposed method uses a modified spectral subtraction with noise estimation by a least-squares line method and with an overweighting gain per subband with nonlinear structure, where the overweighting gain is used for suppressing the residue of musical noise and the subband is used for applying the weighted values according to the change of signals. The enhanced speech by our method has the following properties: 1) the speech intelligibility can be assured reliably; 2) the musical noise can be reduced efficiently. Various assessments confirmed that the performance of the proposed method was better than that of the compared methods in various noise-level conditions. Especially, the proposed method showed good results even at low SNR.
    01/2007;
  • Sung-il Jung, Younghun Kwon, Sung-il Yang
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    ABSTRACT: In this paper, we suggest a speech enhancement method which can be applied in various noise environments. This method uses a wavelet packet transform (WPT) and a best fitting regression line (BFRL) in order to accurately estimate parameters for the spectral subtraction method based on the time-varying gain function. It should be noted that our method does not use the statistical information of pause region detected by voice activity detector. The evaluation is performed on various environments where the noisy speech are between SNR -5 ~ 15 dB, in various noises. We compare the performance of the proposed method, with that of magnitude spectral subtraction in WPT and nonlinear magnitude spectral subtraction in WPT. We can see that the performance of the proposed method is better than that of any other methods, with regard to objective test (segmental SNR, weighted spectral slope), spectrogram analysis, and subjective one (mean opinion score). Especially, our method showed reliable result even at low SNR
    Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on; 06/2006
  • Source
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    ABSTRACT: Recently, Montemurro et al. showed that there would be a long-range correlation in Shakespeare’s 36 plays written in English. However it is not clear that similar result can be found in other language which has very different grammar structure from English. So we examine whether there exists a long-range correlation in Shakespeare’s plays translated into Korean which would have very different syntactic rules from English. Additionally, we perform the same experiment for 12 novels written by four Korean popular writers in order to see the difference due to the styles of the writers. As a result, we find Hurst’s exponents of 0.632 ± 0.03 in Shakespeare’s plays translated in Korean. Also we find the similar values of Hurst’s exponent even to the novels of the Korean popular writers. It implies that regardless of languages, there exists a long-range correlation in literary corpora.
    Chaos Solitons & Fractals 01/2006; · 1.50 Impact Factor
  • Sung-il JUNG, Younghun KWON, Sung-il YANG
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    ABSTRACT: In this letter, we suggest a noise estimation method which can be applied for speech enhancement in various noise environments. The proposed method consists of the following two main processes to analyze and estimate efficiently the noise from the noisy speech. First, a least-squares line is used, which is obtained by applying coefficient magnitudes in node with a uniform wavelet packet transform to a least squares method. Next, a differential forgetting factor and a correlation coefficient per subband are applied, where each subband consists of several nodes with the uniform wavelet packet transform. In particular, this approach has the ability to update noise estimation by using the estimated noise at the previous frame only instead of employing the statistical information of long past frames and explicit nonspeech frames detection consisted of noise signals. In objective assessments, we observed that the performance of the proposed method was better than that of the compared methods. Furthermore, our method showed a reliable result even at low SNR.
    01/2006;
  • Sungwook Chang, Y. Kwon, Sung-Il Yang, I-Jae Kim
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    ABSTRACT: We consider the non-stationary or colored noise estimation by wavelet thresholding method. First, we propose node dependent thresholding for adaptation in colored or non-stationary noise. Next, we suggest a noise estimation method based on spectral entropy using histogram of intensity instead of estimation method based on median absolute deviation (MAD). We use a modified hard thresholding to alleviate time-frequency discontinuities. The proposed methods are evaluated on various noise conditions - white Gaussian noise, car interior noise, F-16 cockpit noise, pink noise, speech babble noise. We compare our proposed methods with the conventional one with level dependent thresholding based on MAD
    Acoustics, Speech, and Signal Processing, 2002. Proceedings. (ICASSP '02). IEEE International Conference on; 02/2002
  • 7th International Conference on Spoken Language Processing, ICSLP2002 - INTERSPEECH 2002, Denver, Colorado, USA, September 16-20, 2002; 01/2002
  • I-Jae Kim, Sung-Il Yang, Y. Kwon
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    ABSTRACT: In this paper, the authors propose an adaptive wavelet threshold for noise cancellation. For this, they use a threshold value which minimizes Bayesian risk. And using entropy, they part the noisy signal into an unvoiced signal section and the other signal section is used to apply each the threshold value for each section. Experimental results show that proposed algorithm is more efficient
    Industrial Electronics, 2001. Proceedings. ISIE 2001. IEEE International Symposium on; 02/2001
  • Sungwook Chang, Y. Kwon, Sung-Il Yang
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    ABSTRACT: In a real environment, additive noise will corrupt input speech for speech recognition. In this paper, the authors propose a noise suppression method on the wavelet packet domain as a front-end pre-processor for robust speech recognition. They focus on the enhancement of the formant characteristic to input speech. Suppose, one has observations y<sub>i</sub>=f(t<sub>i</sub>)+ σ·z<sub>i </sub>, i=0, 1, …, n-1, where f(t<sub>i</sub>) is the speech signal and z<sub>i</sub> is i.i.d. white Gaussian noise (AWGN). And assume that one has an available library L of orthogonal bases, such as wavelet packet bases. Using these assumptions, the authors enhance the formant characteristic as well as SNR by adjusting each node variance from adapted wavelet packet transform (AWPT) tree. Experimental result shows an enhancement of SNR from 3.58 dB to 8.66 dB. Also, phoneme recognition performance is improved more than 6%. It confirms the robustness of proposed noise suppression method against additive white Gaussian noise
    Industrial Electronics, 2001. Proceedings. ISIE 2001. IEEE International Symposium on; 02/2001
  • Sungwook Chang, Y. Kwon, Sung-Il Yang
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    ABSTRACT: The speech signal is decomposed through adapted local trigonometric transforms. The decomposed signal is classified by M uniform sub-bands for each subinterval. The energy of each sub-band is used as a speech feature. This feature is applied to vector quantisation and the hidden Markov model. The new speech feature shows a slightly better recognition rate than the cepstrum for speaker independent speech recognition. The new speech feature also shows a lower standard deviation between speakers than does the cepstrum
    Electronics Letters 12/1998; · 1.04 Impact Factor