• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.215-218
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• 2003

The method of vocal tract normalization has been known as a successful method for improving the accuracy of speech recognition. A frequency warping procedure based low complexity and maximum likelihood has been generally applied for vocal tract normalization. In this paper, we propose a new power spectrum warping procedure that can be improve on vocal tract normalization performance than a frequency warping procedure. A mechanism for implementing this method can be simply achieved by modifying the power spectrum of filter bank in Mel-frequency cepstrum feature(MFCC) analysis. Experimental study compared our Proposal method with the well-known frequency warping method. The results have shown that the power spectrum warping is better 50% about the recognition performance than the frequency warping.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.4
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• pp.103-111
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• 2004

The method of speaker normalization has been known as the successful method for improving the accuracy of speech recognition at speaker independent speech recognition system. A frequency warping approach is widely used method based on maximum likelihood for speaker normalization. This paper propose a new power spectrum warping approach to making improvement of speaker normalization better than a frequency warping. Th power spectrum warping uses Mel-frequency cepstrum analysis(MFCC) and is a simple mechanism to performing speaker normalization by modifying the power spectrum of Mel filter bank in MFCC. Also, this paper propose the hybrid VTN combined the Power spectrum warping and a frequency warping. Experiment of this paper did a comparative analysis about the recognition performance of the SKKU PBW DB applied each speaker normalization approach on baseline system. The experiment results have shown that a frequency warping is 2.06%, the power spectrum is 3.06%, and hybrid VTN is 4.07% word error rate reduction as of word recognition performance of baseline system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.8C
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• pp.801-805
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• 2006

A normalization approach to coefficient adaptation in the escalator(ESC) filter structure that conventionally employs least mean square(LMS) algorithm is introduced. Using Taylor's expansion of the local error signal, a normalized form of the ESC-LMS algorithm is derived. Compared with the computational complexity of the conventional ESC-LMS algorithm employs input power estimation for time-varying convergence coefficient using a single-pole low-pass filter, the computational complexity of the proposed method can be reduced by 50% without performance degradation.

• ETRI Journal
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• v.38 no.6
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• pp.1190-1196
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• 2016

We propose a new bandpass filter (BPF)-based online channel normalization method to dynamically suppress channel distortion when the speech and channel noise components are unknown. In this method, an adaptive modulation frequency filter is used to perform channel normalization, whereas conventional modulation filtering methods apply the same filter form to each utterance. In this paper, we only normalize the two mel frequency cepstral coefficients (C0 and C1) with large dynamic ranges; the computational complexity is thus decreased, and channel normalization accuracy is improved. Additionally, to update the filter weights dynamically, we normalize the learning rates using the dimensional power of each frame. Our speech recognition experiments using the proposed BPF-based blind channel normalization method show that this approach effectively removes channel distortion and results in only a minor decline in accuracy when online channel normalization processing is used instead of batch processing

• Journal of applied mathematics & informatics
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• v.10 no.1_2
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• pp.1-15
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• 2002

In this paper an important stability property of the extremes under power normalizations is discussed. It is proved that the restricted convergence of the Power normalized extremes on an arbitrary nondegenerate interval implies the weak convergence. Moreover, this implication, in an important practical situation, is obtained when the sample size is considered as a random variable distributed geometrically with mean n.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.11C
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• pp.1052-1061
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• 2003

This paper proposes a novel normalization scheme in the state metric calculation unit for the Block-wise MAP Turbo decoder. The proposed scheme subtracts one of four metrics from the state metrics in a trellis stage and shifts, if necessary, those metrics for normalization. The proposed architecture can reduce power consumption and memory requirement by reducing the number of the state metrics by one in a trellis stage in the Block-wise MAP decoder which requires an intensive state metric calculations. Simulation results show that dynamic power has been reduced by 17.9% and area has been reduced by 6.6% in the Turbo decoder employing the proposed normalization scheme, when compared to the conventional Block-wise MAP Turbo decoders.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.4028-4042
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• 2021

Aiming at the problem of software defect prediction difficulty caused by insufficient software defect marker samples and unbalanced classification, a semi-supervised software defect prediction model based on a tri-training algorithm was proposed by combining feature normalization, over-sampling technology, and a Tri-training algorithm. First, the feature normalization method is used to smooth the feature data to eliminate the influence of too large or too small feature values on the model's classification performance. Secondly, the oversampling method is used to expand and sample the data, which solves the unbalanced classification of labelled samples. Finally, the Tri-training algorithm performs machine learning on the training samples and establishes a defect prediction model. The novelty of this model is that it can effectively combine feature normalization, oversampling techniques, and the Tri-training algorithm to solve both the under-labelled sample and class imbalance problems. Simulation experiments using the NASA software defect prediction dataset show that the proposed method outperforms four existing supervised and semi-supervised learning in terms of Precision, Recall, and F-Measure values.

• Earthquakes and Structures
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• v.2 no.2
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• pp.171-187
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• 2011

Rational scaling of design earthquake ground motions for tall buildings is essential for safer, risk-based design of tall buildings. This paper provides the structural designers with an insight for more rational scaling based on drift and input energy demands. Since a resonant sinusoidal motion can be an approximate critical excitation to elastic and inelastic structures under the constraint of acceleration or velocity power, a resonant sinusoidal motion with variable period and duration is used as an input wave of the near-field and far-field ground motions. This enables one to understand clearly the relation of the intensity normalization index of ground motion (maximum acceleration, maximum velocity, acceleration power, velocity power) with the response performance (peak interstory drift, total input energy). It is proved that, when the maximum ground velocity is adopted as the normalization index, the maximum interstory drift exhibits a stable property irrespective of the number of stories. It is further shown that, when the velocity power is adopted as the normalization index, the total input energy exhibits a stable property irrespective of the number of stories. It is finally concluded that the former property on peak drift can hold for the practical design response spectrum-compatible ground motions.

• YAKHAK HOEJI
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• v.38 no.4
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• pp.366-371
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• 1994

To improve the identification power of TLC/HPTLC the in situ reflectance spectra obtained directly from plates with commercial scanner are used. The spectrum normalization should be carried out prior to comparing and searching the spectra from library for the identification of compounds. Because the reflectance does not obey the Lambert-Beer's law, there arise some problems in normalization. These problems could be solved to some extent by normalizing the spectra with regression methods. The spectra are manipulated with the regression function of a curve obtained from the correlation plot. When the parabola was used as the manipulating function, the spectra were identified with the accuracy of 97% and this result was better than that of conventionally used the point and area normalization method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.1
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• pp.77-79
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• 2016

In this paper, we consider hierarchical modulation (HM), which has been widely exploited in digital broadcasting systems. In HM, each independent data stream is mapped to the modulation symbol with different transmission power and normalization factors of conventional M-QAM cannot be used. In this paper, we derive the method and formula for exact normalization factor of three-level hierarchical 64QAM.