• Title/Summary/Keyword: Noise Robust

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Filtered-x LMS Algorithm for noise and vibration control system (잡음 및 진동제어시스템을 위한 Filtered -x LMS 알고리즘)

  • kim, soo-yong;Jee, suk-kun
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2009.05a
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    • pp.697-702
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    • 2009
  • Filtered-x LMS algorithm maybe the most popular control algorithm used in DSP implementations of active noise and vibration control system. The algorithm converges on a timescale comparable to the response time of the system to be controlled, and is found to be very robust. If the pure tone reference signal is synchronously sampled, it is found that the behavior of the adaptive system can be completely described by a matrix of linear, time invariant, transfer functions. This is used to explain the behavior observed in simulations of a simplified single input, single output adaptive system, which retains many of the properties of the multichannel algorithm.

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DNN based Robust Speech Feature Extraction and Signal Noise Removal Method Using Improved Average Prediction LMS Filter for Speech Recognition (음성 인식을 위한 개선된 평균 예측 LMS 필터를 이용한 DNN 기반의 강인한 음성 특징 추출 및 신호 잡음 제거 기법)

  • Oh, SangYeob
    • Journal of Convergence for Information Technology
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    • v.11 no.6
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    • pp.1-6
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    • 2021
  • In the field of speech recognition, as the DNN is applied, the use of speech recognition is increasing, but the amount of calculation for parallel training needs to be larger than that of the conventional GMM, and if the amount of data is small, overfitting occurs. To solve this problem, we propose an efficient method for robust voice feature extraction and voice signal noise removal even when the amount of data is small. Speech feature extraction efficiently extracts speech energy by applying the difference in frame energy for speech and the zero-crossing ratio and level-crossing ratio that are affected by the speech signal. In addition, in order to remove noise, the noise of the speech signal is removed by removing the noise of the speech signal with an average predictive improved LMS filter with little loss of speech information while maintaining the intrinsic characteristics of speech in detection of the speech signal. The improved LMS filter uses a method of processing noise on the input speech signal by adjusting the active parameter threshold for the input signal. As a result of comparing the method proposed in this paper with the conventional frame energy method, it was confirmed that the error rate at the start point of speech is 7% and the error rate at the end point is improved by 11%.

Own-ship noise cancelling method for towed line array sonars using a beam-formed reference signal (기준 빔 신호를 이용한 예인선배열 소나의 자함 소음 제거 기법)

  • Lee, Dan-Bi
    • The Journal of the Acoustical Society of Korea
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    • v.39 no.6
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    • pp.559-567
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    • 2020
  • This paper proposes a noise cancelling algorithm to remove own-ship noise for a towed array sonar. Extra beamforming is performed using partial channels of the acoustic array to get a reference beam signal robust to the noise bearing. Frequency domain Adaptive Noise Cancelling (ANC) is applied based on Normalized Least Mean Square (NLMS) algorithm using the reference beam. The bearing of own-ship noise is estimated from the coherence between the reference beam and input beam signals. Own-ship noise level is calculated using a beampattern of the noise with estimated steering angle, which prevents loss of a target signal by determining whether to update a filter so that removed signal level does not exceed the estimated noise level. Simulation results show the proposed algorithm maintains its performance when the own-ship gets out off its bearing 40 % more than the conventional algorithm's limit and detects the target even when the frequency of the target signal is same with the frequency of the own-ship signal.

Noise-Robust Speaker Recognition Using Subband Likelihoods and Reliable-Feature Selection

  • Kim, Sung-Tak;Ji, Mi-Kyong;Kim, Hoi-Rin
    • ETRI Journal
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    • v.30 no.1
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    • pp.89-100
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    • 2008
  • We consider the feature recombination technique in a multiband approach to speaker identification and verification. To overcome the ineffectiveness of conventional feature recombination in broadband noisy environments, we propose a new subband feature recombination which uses subband likelihoods and a subband reliable-feature selection technique with an adaptive noise model. In the decision step of speaker recognition, a few very low unreliable feature likelihood scores can cause a speaker recognition system to make an incorrect decision. To overcome this problem, reliable-feature selection adjusts the likelihood scores of an unreliable feature by comparison with those of an adaptive noise model, which is estimated by the maximum a posteriori adaptation technique using noise features directly obtained from noisy test speech. To evaluate the effectiveness of the proposed methods in noisy environments, we use the TIMIT database and the NTIMIT database, which is the corresponding telephone version of TIMIT database. The proposed subband feature recombination with subband reliable-feature selection achieves better performance than the conventional feature recombination system with reliable-feature selection.

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Mid frequency - DCT focus measure operator for the robust autofocus (노이즈에 둔감한 밴드패스 이산 코사인 초점 값 연산자)

  • Lee, Sang-Yong;Park, Sang-Soo;Kim, Soo-Won
    • Journal of the Institute of Electronics Engineers of Korea SD
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    • v.43 no.12 s.354
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    • pp.8-14
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    • 2006
  • This paper proposed noise insensitive 4*4 mid frequency-OCT (MF-DCT) focus measure operator. Proposed operator enhanced low power 8*8 MDCT operator to have 4*4 rotationally same form for Gaussian noise. MF-DCT operator acting like band-pass filter suppresses both low-frequency signal useless for focus measure and high-frequency signal affected by impulsive noise. Also it is proved to be linear because it uses the energy of band-pass filtered signal as focus measure. Experimental result shows its superiority by comparing AUM with traditional operators.

Performance Analysis of Correntropy-Based Blind Algorithms Robust to Impulsive Noise (충격성 잡음에 강인한 코렌트로피 기반 블라인드 알고리듬의 성능분석)

  • Kim, Namyong
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.40 no.12
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    • pp.2324-2330
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    • 2015
  • In blind signal processing in impulsive noise environment the maximum cross-correntropy (MCC) algorithm shows superior performance compared to MSE-based algorithms. But optimum weight conditions of MCC algorithm and its properties related with robustness to impulsive noise have not been studied sufficiently. In this paper, through the analysis of the behavior of its optimum weight and the relationship with the MSE-based LMS algorithm, it is shown that the optimum weight of MCC and MSE-based LMS have an equal solution. Also the factor that keeps optimum weight of MCC undisturbed and stable under impulsive noise is proven to be the magnitude controlled input through simulation.

Position Optimization of Strain Gauge on Blades

  • Choi, Byeong-Keun;Lee, Hyun-Seob;Yang, Bo-Suk;Mignolet, Marc P.
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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    • 2002.11b
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    • pp.422-427
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    • 2002
  • This paper focuses on the formulation and validation of an automatic strategy for the selection of the locations and directions of strain gauges to capture at best the modal response of a blade in a series of modes. These locations and directions are selected to render the strain measurements as robust as possible with respect to random mispositioning of the gauges and gauge failures. The approach relies on the evaluation of the signal-to-noise ratios of the gauge measurements from finite element strain data and includes the effects of gauge size. A genetic algorithm is used to find the strain gauge locations-directions that lead to the largest possible value of the smallest modal strain signal-to-noise ratio, in the absence of gauge failure, or of its expected value when gauge failure is possible. A fan blade is used to exemplify the applicability of the proposed methodology and to demonstrate the effects of the essential parameters of the problem, i.e. the mispositioning level, the probability of gauge failure, and the number of gauges.

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A Kernel based Possibilistic Approach for Clustering and Image Segmentation (클러스터링 및 영상 분할을 위한 커널 기반의 Possibilistic 접근 방법)

  • Choi, Kil-Soo;Choi, Byung-In;Rhee, Chung-Hoon
    • Journal of the Korean Institute of Intelligent Systems
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    • v.14 no.7
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    • pp.889-894
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    • 2004
  • The fuzzy kernel c-means (FKCM) algorithm, which uses a kernel function, can obtain more desirable clustering results than fuzzy c-means (FCM) for not only spherical data but also non-spherical data. However, it can be sensitive to noise as in the FCM algorithm. In this paper, a kernel function is applied to the possibilistic c-means (PCM) algorithm and is shown to be robust for data with additive noise. Several experimental results show that the proposed kernel possibilistic c-means (KPCM) algorithm out performs the FKCM algorithm for general data with additive noise.

A Real-Time Implementation of Speech Recognition System Using Oak DSP core in the Car Noise Environment (자동차 환경에서 Oak DSP 코어 기반 음성 인식 시스템 실시간 구현)

  • Woo, K.H.;Yang, T.Y.;Lee, C.;Youn, D.H.;Cha, I.H.
    • Speech Sciences
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    • v.6
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    • pp.219-233
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    • 1999
  • This paper presents a real-time implementation of a speaker independent speech recognition system based on a discrete hidden markov model(DHMM). This system is developed for a car navigation system to design on-chip VLSI system of speech recognition which is used by fixed point Oak DSP core of DSP GROUP LTD. We analyze recognition procedure with C language to implement fixed point real-time algorithms. Based on the analyses, we improve the algorithms which are possible to operate in real-time, and can verify the recognition result at the same time as speech ends, by processing all recognition routines within a frame. A car noise is the colored noise concentrated heavily on the low frequency segment under 400 Hz. For the noise robust processing, the high pass filtering and the liftering on the distance measure of feature vectors are applied to the recognition system. Recognition experiments on the twelve isolated command words were performed. The recognition rates of the baseline recognizer were 98.68% in a stopping situation and 80.7% in a running situation. Using the noise processing methods, the recognition rates were enhanced to 89.04% in a running situation.

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Optimal Placement of Strain Gauge for Vibration Measurement : Formulation and Assessment (진동측정을 위한 스트레인 게이지 설치위치 최적화 : 최적화 방법 및 평가)

  • 최창림;양보석;최병근
    • Transactions of the Korean Society for Noise and Vibration Engineering
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    • v.14 no.8
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    • pp.757-766
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    • 2004
  • This paper focuses on the formulation and validation of an automatic strategy to select the optimal location and direction of strain gauges for the measurement of the modal response. These locations and directions are important to render the strain measurements as robust as possible when a random mispositioning of the gauges and gauge failures are expected. The approach relies on the evaluation of the signal-to-noise ratios of the gauge measurements from strain data of finite element. The multi-step optimization strategy including genetic algorithm is used to find the strain gauge locations-directions that maximize the smallest modal strain signal-to-noise ratio in the absence of gauge failure or its expected value when gauge failure is possible. A flat Plate is used to prove the applicability of the proposed methodology and to demonstrate the effects of the essential parameters of the problem such as the mispositioning level, the probability of gauge failure, and the number of gauges.