• The Journal of the Acoustical Society of Korea
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• v.30 no.3
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• pp.123-128
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• 2011

In this paper, we propose the improved noise power estimation in speech enhancement under various noise environments. The previous MS algorithm tracking the minimum value of finite search window uses the optimal power spectrum of signal for smoothing and adopts minimum probability. From the investigation of the previous MS-based methods it can be seen that a fixed size of the minimum search window is assumed regardless of the various environment. To achieve the different search window size, we use the noise classification algorithm based on the Gaussian mixture model (GMM). Performance of the proposed enhancement algorithm is evaluated by ITU-T P.862 perceptual evaluation of speech quality (PESQ) under various noise environments. Based on this, we show that the proposed algorithm yields better result compared to the conventional MS method.

• The Journal of the Acoustical Society of Korea
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• v.26 no.4
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• pp.153-158
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• 2007

This paper presents a new approach to noise estimation to improve speech enhancement in non-stationary noisy environments. The proposed method combines the two separate noise power estimates provided by the minimum statistics (MS) for speech presence and soft decision (SD) for speech absence in accordance with SAP (Speech Absence Probability) on a separate frequency bin. The performance of the proposed algorithm is evaluated by the subjective test under various noise environments and yields better results compared with the conventional MS or SD-based schemes.

• The Journal of the Acoustical Society of Korea
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• v.29 no.3
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• pp.200-208
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• 2010

In this work, the minimum statistics (MS) algorithm is combined with the codebook driven short-term predictor parameter estimation (CDSTP) to design a speech enhancement algorithm that is robust against various background noise environments. The MS algorithm functions well for the stationary noise but relatively not for the non-stationary noise. The CDSTP works efficiently for the non-stationary noise, but not for the noise that was not considered in the training stage. Thus, we propose to combine CDSTP and MS. Compared with the single use of MS and CDSTP, the proposed method produces better perceptual evaluation of speech quality (PESQ) score, and especially works excellent for the mixed background noise between stationary and non-stationary noises.

• The Journal of the Acoustical Society of Korea
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• v.28 no.1
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• pp.77-83
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• 2009

This paper proposes a new noise-biased compensation of minimum statistics(MS) method using a nonlinear function and a priori speech absence probability(SAP) for speech enhancement in non-stationary noisy environments. The minimum statistics(MS) method is well known technique for noise power estimation in non-stationary noisy environments. It tends to bias the noise estimate below that of true noise level. The proposed method is combined with an adaptive parameter based on a sigmoid function and a priori speech absence probability (SAP) for biased compensation. Specifically. we apply the adaptive parameter according to the a posteriori SNR. In addition, when the a priori SAP equals unity, the adaptive biased compensation factor separately increases ${\delta}_{max}$ each frequency bin, and vice versa. We evaluate the estimation of noise power capability in highly non-stationary and various noise environments, the improvement in the segmental signal-to-noise ratio (SNR), and the Itakura-Saito Distortion Measure (ISDM) integrated into a spectral subtraction (SS). The results shows that our proposed method is superior to the conventional MS approach.

• Journal of The Korean Astronomical Society
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• v.37 no.5
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• pp.509-515
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• 2004

We present results from an extensive synthetic observation analysis of numerically-simulated radio galaxy (RG) jets. This analysis is based on the first three-dimensional simulations to treat cosmic ray acceleration and transport self-consistently within a magnetohydrodynamical calculation. We use standard observational techniques to calculate both minimum-energy and inverse-Compton field values for our simulated objects. The latter technique provides meaningful information about the field. Minimum-energy calculations retrieve reasonable field estimates in regions physically close to the minimum-energy partitioning, though the technique is highly susceptible to deviations from the underlying assumptions. We also study the reliability of published rotation measure analysis techniques. We find that gradient alignment statistics accurately reflect the physical situation, and can uncover otherwise hidden information about the source. Furthermore, correlations between rotation measure (RM) and position angle (PA) can be significant even when the RM is completely dominated by an external cluster medium.

• The Journal of the Acoustical Society of Korea
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• v.27 no.7
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• pp.386-393
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• 2008

This paper proposes a new approach for speech enhancement in highly nonstationary noisy environments. The spectral subtraction (SS) is a well known technique for speech enhancement in stationary noisy environments. However, in real world, noise is mostly nonstationary. The proposed method uses an auto control parameter for an adaptive threshold to work well in highly nonstationary noisy environments. Especially, the auto control parameter is affected by a linear function associated with an a posteriori signal to noise ratio (SNR) according to the increase or the decrease of the noise level. The proposed algorithm is combined with spectral subtraction (SS) using a hangover scheme (HO) for speech enhancement. The performances of the proposed method are evaluated ITU-T P.835 signal distortion (SIG) and the segment signal to-noise ratio (SNR) in various and highly nonstationary noisy environments and is superior to that of conventional spectral subtraction (SS) using a hangover (HO) and SS using a minimum statistics (MS) methods.