• 제목/요약/키워드: Noisy

검색결과 1,570건 처리시간 0.028초

Noisy Speech Recognition Based on Noise-Adapted HMMs Using Speech Feature Compensation

  • Chung, Yong-Joo
    • 융합신호처리학회논문지
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    • 제15권2호
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    • pp.37-41
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    • 2014
  • The vector Taylor series (VTS) based method usually employs clean speech Hidden Markov Models (HMMs) when compensating speech feature vectors or adapting the parameters of trained HMMs. It is well-known that noisy speech HMMs trained by the Multi-condition TRaining (MTR) and the Multi-Model-based Speech Recognition framework (MMSR) method perform better than the clean speech HMM in noisy speech recognition. In this paper, we propose a method to use the noise-adapted HMMs in the VTS-based speech feature compensation method. We derived a novel mathematical relation between the train and the test noisy speech feature vector in the log-spectrum domain and the VTS is used to estimate the statistics of the test noisy speech. An iterative EM algorithm is used to estimate train noisy speech from the test noisy speech along with noise parameters. The proposed method was applied to the noise-adapted HMMs trained by the MTR and MMSR and could reduce the relative word error rate significantly in the noisy speech recognition experiments on the Aurora 2 database.

잡음음성 음향모델 적응에 기반한 잡음에 강인한 음성인식 (Noise Robust Speech Recognition Based on Noisy Speech Acoustic Model Adaptation)

  • 정용주
    • 말소리와 음성과학
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    • 제6권2호
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    • pp.29-34
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    • 2014
  • In the Vector Taylor Series (VTS)-based noisy speech recognition methods, Hidden Markov Models (HMM) are usually trained with clean speech. However, better performance is expected by training the HMM with noisy speech. In a previous study, we could find that Minimum Mean Square Error (MMSE) estimation of the training noisy speech in the log-spectrum domain produce improved recognition results, but since the proposed algorithm was done in the log-spectrum domain, it could not be used for the HMM adaptation. In this paper, we modify the previous algorithm to derive a novel mathematical relation between test and training noisy speech in the cepstrum domain and the mean and covariance of the Multi-condition TRaining (MTR) trained noisy speech HMM are adapted. In the noisy speech recognition experiments on the Aurora 2 database, the proposed method produced 10.6% of relative improvement in Word Error Rates (WERs) over the MTR method while the previous MMSE estimation of the training noisy speech produced 4.3% of relative improvement, which shows the superiority of the proposed method.

시공간적 연속성을 이용한 오염된 식생지수(GIMMS NDVI) 화소의 탐지 및 보정 기법 개발 (Detection and Correction of Noisy Pixels Embedded in NDVI Time Series Based on the Spatio-temporal Continuity)

  • 박주희;조아라;강전호;서명석
    • 대기
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    • 제21권4호
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    • pp.337-347
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    • 2011
  • In this paper, we developed a detection and correction method of noisy pixels embedded in the time series of normalized difference vegetation index (NDVI) data based on the spatio-temporal continuity of vegetation conditions. For the application of the method, 25-year (1982-2006) GIMMS (Global Inventory Modeling and Mapping Study) NDVI dataset over the Korean peninsula were used. The spatial resolution and temporal frequency of this dataset are $8{\times}8km^2$ and 15-day, respectively. Also the land cover map over East Asia is used. The noisy pixels are detected by the temporal continuity check with the reference values and dynamic threshold values according to season and location. In general, the number of noisy pixels are especially larger during summer than other seasons. And the detected noisy pixels are corrected by the iterative method until the noisy pixels are completely corrected. At first, the noisy pixels are replaced by the arithmetic weighted mean of two adjacent NDVIs when the two NDVI are normal. After that the remnant noisy pixels are corrected by the weighted average of NDVI of the same land cover according to the distance. After correction, the NDVI values and their variances are increased and decreased by 5% and 50%, respectively. Comparing to the other correction method, this correction method shows a better result especially when the noisy pixels are occurred more than 2 times consistently and the temporal change rates of NDVI are very high. It means that the correction method developed in this study is superior in the reconstruction of maximum NDVI and NDVI at the starting and falling season.

Statistical Approach to Noisy Band Removal for Enhancement of HIRIS Image Classification

  • Huan, Nguyen Van;Kim, Hak-Il
    • 대한원격탐사학회:학술대회논문집
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    • 대한원격탐사학회 2008년도 춘계학술대회 논문집
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    • pp.195-200
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    • 2008
  • The accuracy of classifying pixels in HIRIS images is usually degraded by noisy bands since noisy bands may deform the typical shape of spectral reflectance. Proposed in this paper is a statistical method for noisy band removal which mainly makes use of the correlation coefficients between bands. Considering each band as a random variable, the correlation coefficient measures the strength and direction of a linear relationship between two random variables. While the correlation between two signal bands is high, existence of a noisy band will produce a low correlation due to ill-correlativeness and undirectedness. The application of the correlation coefficient as a measure for detecting noisy bands is under a two-pass screening scheme. This method is independent of the prior knowledge of the sensor or the cause resulted in the noise. The classification in this experiment uses the unsupervised k-nearest neighbor algorithm in accordance with the well-accepted Euclidean distance measure and the spectral angle mapper measure. This paper also proposes a hierarchical combination of these measures for spectral matching. Finally, a separability assessment based on the between-class and within-class scatter matrices is followed to evaluate the performance.

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Ddenoising of a Positive Signal with White Gaussian Noise by Using Wavelet Transform

  • Koo, Ja-Yong
    • The Journal of the Acoustical Society of Korea
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    • 제17권1E호
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    • pp.30-35
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    • 1998
  • Given a noisy sampled at equispaced points with white noise, we consider problems where the signal to be recovered is known to be positive; for example, images, chemical spectra or other measurements of intensities. Shrinking noisy wavelet coefficients via thresholding offers very attractive alternatives to existing methods of recovering signals from noisy data. In this paper, we propose a method of recovering the original signal from a corrupted noisy signal, guaranteeing the recovered signal positive. We first obtain wavelet coefficients by thresholding, and use a nonlinear optimization to find the denoised signal which must be positive. Numerical examples are used to illustrate the performance of the proposed algorithm.

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시간영역 수동제어기의 미세떨림현상 제거 (Removing the Noisy Behavior of the Time Domain Passivity Controller)

  • 유지환
    • 제어로봇시스템학회논문지
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    • 제12권4호
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    • pp.380-388
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    • 2006
  • A noisy behavior of the time domain passivity controller during the period of low velocity is analyzed. Main reasons of the noisy behavior are investigated through a simulation with a one-DOF (Degree of Freedom) haptic interface model. It is shown that the PO/PC is ineffective in dissipating the produced energy when the sign of the velocity, which is numerically calculated from the measured position, is suddenly changed, and when this velocity is zero. These cases happen during the period of low velocity due to the limited resolution of the position sensor. New methods, ignoring the produced energy from the velocity sign change, and holding the control force while the velocity is zero, are proposed for removing the noisy behavior. The feasibility of the developed methods is proved with both a simulation and a real experiment.

Issues and Empirical Results for Improving Text Classification

  • Ko, Young-Joong;Seo, Jung-Yun
    • Journal of Computing Science and Engineering
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    • 제5권2호
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    • pp.150-160
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    • 2011
  • Automatic text classification has a long history and many studies have been conducted in this field. In particular, many machine learning algorithms and information retrieval techniques have been applied to text classification tasks. Even though much technical progress has been made in text classification, there is still room for improvement in text classification. In this paper, we will discuss remaining issues in improving text classification. In this paper, three improvement issues are presented including automatic training data generation, noisy data treatment and term weighting and indexing, and four actual studies and their empirical results for those issues are introduced. First, the semi-supervised learning technique is applied to text classification to efficiently create training data. For effective noisy data treatment, a noisy data reduction method and a robust text classifier from noisy data are developed as a solution. Finally, the term weighting and indexing technique is revised by reflecting the importance of sentences into term weight calculation using summarization techniques.

향상된 JA 방식을 이용한 다 모델 기반의 잡음음성인식에 대한 연구 (A Study on the Noisy Speech Recognition Based on Multi-Model Structure Using an Improved Jacobian Adaptation)

  • 정용주
    • 음성과학
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    • 제13권2호
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    • pp.75-84
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    • 2006
  • Various methods have been proposed to overcome the problem of speech recognition in the noisy conditions. Among them, the model compensation methods like the parallel model combination (PMC) and Jacobian adaptation (JA) have been found to perform efficiently. The JA is quite effective when we have hidden Markov models (HMMs) already trained in a similar condition as the target environment. In a previous work, we have proposed an improved method for the JA to make it more robust against the changing environments in recognition. In this paper, we further improved its performance by compensating the delta-mean vectors and covariance matrices of the HMM and investigated its feasibility in the multi-model structure for the noisy speech recognition. From the experimental results, we could find that the proposed improved the robustness of the JA and the multi-model approach could be a viable solution in the noisy speech recognition.

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A Noise Reduction Method Combined with HMM Composition for Speech Recognition in Noisy Environments

  • Shen, Guanghu;Jung, Ho-Youl;Chung, Hyun-Yeol
    • 대한임베디드공학회논문지
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    • 제3권1호
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    • pp.1-7
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    • 2008
  • In this paper, a MSS-NOVO method that combines the HMM composition method with a noise reduction method is proposed for speech recognition in noisy environments. This combined method starts with noise reduction with modified spectral subtraction (MSS) to enhance the input noisy speech, then the noise and voice composition (NOVO) method is applied for making noise adapted models by using the noise in the non-utterance regions of the enhanced noisy speech. In order to evaluate the effectiveness of our proposed method, we compare MSS-NOVO method with other methods, i.e., SS-NOVO, MWF-NOVO. To set up the noisy speech for test, we add White noise to KLE 452 database with different SNRs range from 0dB to 15dB, at 5dB intervals. From the tests, MSS-NOVO method shows average improvement of 66.5% and 13.6% compared with the existing SS-NOVO method and MWF-NOVO method, respectively. Especially our proposed MSS-NOVO method shows a big improvement at low SNRs.

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컨테이너 기반 가상화에서 격리성 보장을 위한 취약성 고찰 (A Study on Vulnerability for Isolation Guarantee in Container-based Virtualization)

  • 염다연;신동천
    • 융합보안논문지
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    • 제23권4호
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    • pp.23-32
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    • 2023
  • 클라우드 컴퓨팅 환경에서 컨테이너 기반 가상화는 게스트 운영체제 대신에 호스트 운영체제를 공유함으로써 가벼운 사용감으로 가상머신 기반 가상화 기술의 대안으로 많은 관심을 받고 있다. 그러나 호스트 운영체제를 공유함으로써 발생하는 문제점이 컨테이너 기반 가상화의 취약성을 높일 수 있다. 특히 컨테이너들이 자원들을 과도하게 사용함으로 인해 컨테이너들의 격리성을 침해할 수 있는 noisy neighbor problem은 사용자들의 가용성을 위협하게 되므로 보안 문제로 인식할 필요가 있다. 본 논문에서는 컨테이너 기반 가상화 환경에서 noisy neighbor problem이 격리성 보장을 위협할 수 있는 취약성을 고찰한다. 이를 위해 컨테이너 기반의 가상화 구조를 분석하여 기능별 계층에 대한 격리성 보장에 위협이 될 수 있는 취약점을 도출하고 해결 방향과 한계점을 제시한다.