• Journal of Broadcast Engineering
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• v.9 no.1
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• pp.91-94
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• 2004

The vergence controls of the parallel stereoscopic camera need only the disparity information of left and right images in horizontal direction. This paper proposed past and precise disparity value for stereoscopicimage pair in horizontal direction and the algorithm which can abstract disparity information through the HCF(Hybrid Cepstral Filter) for sign information. The proposed disparity information- extracting algorithm can obtain accurate disparity value of horizontal direction and signinformation by using both the one dimension cepstral filter which uses vertical projection data of left and right Image and the two dimension cepstral filter which uses down sampled image.

• ETRI Journal
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• v.26 no.3
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• pp.273-276
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• 2004

We propose a novel feature processing technique which can provide a cepstral liftering effect in the log-spectral domain. Cepstral liftering aims at the equalization of variance of cepstral coefficients for the distance-based speech recognizer, and as a result, provides the robustness for additive noise and speaker variability. However, in the popular hidden Markov model based framework, cepstral liftering has no effect in recognition performance. We derive a filtering method in log-spectral domain corresponding to the cepstral liftering. The proposed method performs a high-pass filtering based on the decorrelation of filter-bank energies. We show that in noisy speech recognition, the proposed method reduces the error rate by 52.7% to conventional feature.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.7C
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• pp.604-610
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• 2010

In this paper we proposed FSFN (Filter bank sub-band energy subtraction based CLSFN) method to improve the recognition performance of the existing CLSFN (Cepstral distance and Log-energy based Silence Feature Normalization). The proposed FSFN reduces the energy of noise components in filter bank sub-band domain when extracting the features from speech data. This leads to extract the enhanced cepstral features and thus improves the accuracy of speech/silence classification using the enhanced cepstral features. Therefore, it can be expected to get improved performance comparing with the existing CLSFN. Experimental results conducted on Aurora 2.0 DB showed that our proposed FSFN method improves the averaged word accuracy of 2% comparing with the conventional CLSFN method, and FSFN combined with CMVN (Cepstral Mean and Variance Normalization) also showed the best recognition performance comparing with others.

• Phonetics and Speech Sciences
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• v.2 no.4
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• pp.145-151
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• 2010

In this paper, a robust feature compensation method is proposed for improving the performance of speech recognition. The proposed method is incorporated into the auto-regressive moving average (ARMA) based feature compensation. We employ variable weights for the ARMA filter according to the degree of speech activity, and pass the normalized cepstral sequence through the weighted ARMA filter. Additionally when normalizing the cepstral sequences in training, the cepstral means and variances are estimated from total training utterances. Experimental results show the proposed method significantly improves the speech recognition performance in the noisy and reverberant environments.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.11B
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• pp.1920-1927
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• 2000

본 논문은 cepstral 필터를 이용하여 지적인 비주얼 센싱을 위한 카메라의 운동 제어법을 제안한다. 화상은 pursuit 운동을 위하여 물체의 옵티컬 플로우가 필요하고, vergence 운동을 위하여 양안시차 정보를 필요로 한다. 그러나, 화상정보에는 올바른 정보와 잘못된 정보가 존재하기 때문에 해의 올바른 시차를 선택해야 하는데, 옵티컬 플로우의 계산에서와 마찬가지로 템플리트 매칭을 이용하여 올바른 정보를 선택한다. 그리고, 화상 중의 하나를 3 조각으로 분할한 후 각각 cepstral 필터링에 의하여 양안시차를 검출한다. 본 논문은 saccade 운동, pursuit 운동, vergence 운동에 관한 제어 알고리즘을 제안하고, 실험에 의하여 알고리즘을 비교 및 분석한다.

• The Journal of the Acoustical Society of Korea
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• v.38 no.1
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• pp.39-46
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• 2019

In this paper, combining features is proposed as a way to enhance the classification accuracy of sounds under noisy environments using the CNN (Convolutional Neural Network) structure. A robust log Mel-filter bank using Wiener filter and PNCCs (Power Normalized Cepstral Coefficients) are extracted to form a 2-dimensional feature that is used as input to the CNN structure. An ebird database is used to classify 43 types of bird species in their natural environment. To evaluate the performance of the combined features under noisy environments, the database is augmented with 3 types of noise under 4 different SNRs (Signal to Noise Ratios) (20 dB, 10 dB, 5 dB, 0 dB). The combined feature is compared to the log Mel-filter bank with and without incorporating the Wiener filter and the PNCCs. The combined feature is shown to outperform the other mentioned features under clean environments with a 1.34 % increase in overall average accuracy. Additionally, the accuracy under noisy environments at the 4 SNR levels is increased by 1.06 % and 0.65 % for shop and schoolyard noise backgrounds, respectively.

• Proceedings of the KSPS conference
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• 2005.04a
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• pp.91-94
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• 2005

The performance of speech recognition is degraded by the mismatch between training and test environments. Many methods have been presented to compensate for additive noise and channel effect in the cepstral domain, and Cepstral Mean Subtraction (CMS) is the representative method among them. Recently, high order cepstral moment normalization method has introduced to improve recognition accuracy. In this paper, we apply high order moment normalization method and smoothing filter for real-time processing. In experiments using Aurora2 DB, we obtained error rate reduction of 49.7% with the proposed algorithm in comparison with baseline system.

• Korean Journal of Optics and Photonics
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• v.15 no.2
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• pp.123-129
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• 2004

This paper concerns auto vergence control of a parallel stereoscopic camera through geometrical analysis. In the construction of a parallel stereoscopic camera, we experimentally demonstrated linear relationship between the key object distance and the amount of vergence control. And we proposed a vergence control system for the stereoscopic camera using binocular disparity information. For the real-time calculation of disparity information, the Hybrid Cepstral filter algorithm, with input data acquired from the vertical projection data and from the down sampling data from the source images, was proposed for precision and high speed processing. With the disparity information algorithm and the vergence control of the parallel stereoscopic camera system, the stereoscopic images become more like those of the human eye.

• The Journal of the Acoustical Society of Korea
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• v.30 no.2
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• pp.100-106
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• 2011

Music recordings have been digitalized such that huge size of music database is available to the public. Thus, the automatic classification system of music genres is required to effectively manage the growing music database. Mel-Frequency Cepstral Coefficient (MFCC) is a popular feature vector for genre classification. In this paper, the combined super-vector with Decorrelated Filter Bank (DFB) and Octave-based Spectral Contrast (OSC) using texture windows is processed by Support Vector Machine (SVM) for genre classification. Even with the lower order of the feature vector, the proposed super-vector produces 4.2 % improved classification accuracy compared with the conventional Marsyas system.

• The Journal of the Acoustical Society of Korea
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• v.7 no.4
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• pp.77-85
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• 1988

Many kinds of conditioning monitoring technique have been studied, so this study has inverstigated the possibility of checking the trend in the fault diagnosis of ball bearing, one of the elements of rotating machine, by applying the cepstral analyisis method using the adaptive noise cancelling (ANC) method. And computer simulation is conducted in order to verify the usefulness of ANC. The optimal adaptation gain in adaptive filter is estimated, the performance of ANC according to the change of the signal to noise ratio and convergence of least mean square algorithm is considered by simulation. It is verified that cepstral analysis using ANC method is more effective than the conventional cepstral analysis method in bearing fault diagnosis.