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14th International Workshop on Acoustic signal Enhancement (IWAENC)

作者： Jukic, Ante van Waterschoot, Toon Gerkmann, Timo Doclo, Simon Carl von Ossietzky Univ Oldenburg Dept Med Phys & Acoust D-26111 Oldenburg Germany Carl von Ossietzky Univ Oldenburg Cluster Excellence Hearing4All D-26111 Oldenburg Germany Katholieke Univ Leuven Dept Elect Engn ESAT STADIUS ETC Leuven Belgium

ISBN: (纸本)9781479968084

Recordings of a speech signal in an enclosed space are typically corrupted with reverberation. In combination with background noise, these effects may severely degrade the speech quality. In this paper we aim to blindly recover the speech signal from the reverberant and possibly noisy observations, where the signals are represented using the convolutive transfer function model in the STFT domain. The problem of blind speech dereverberation is decomposed into a set of independent blind deconvolution problems that we propose to solve using a maximum a posteriori approach and a variational approach, exploiting the sparsity of the speech signal in the STFT domain. The corresponding optimization problems can be solved using an alternating optimization procedure. Experimental results show that the proposed approach based on variational estimation results in consistent improvements of the instrumentally predicted measures of speech enhancement and dereverberation.

关键词： Dereverberation speech enhancement model-based signal processing

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Nonlinear Active Noise Control via model-based Approaches

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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2011年第7期15卷 854-859页

作者： Duong, Sam Chau Kinjo, Hiroshi Oshiro, Naoki Univ Ryukyus Fac Engn 1 Senbaru Nishihara Okinawa 9030213 Japan

Active noise control has attracted much research attention due to its several advantages over passive noise control. This paper introduces two model-based noise canceling techniques, that is, using the Moving Average (MA) model and a feedforward Neural Network (NN) to estimate the signal. The Least Mean Square (LMS) algorithm is used to minimize the error in the MA model while a backpropagation algorithm is employed to optimize the NN. Due to its advantages of good robustness and nonlinear processing, the NN is considered to be suitable for nonlinear signals. In order to reduce computational cost, the backpropagation algorithm in the NN is applied once at each time step with only one iteration. To examine the methods, two real-world problems are considered, one being engine noise and the other road traffic noise. A comparison between the two methods is carried out. Results indicate that both the MA and NN processors are effective in reducing the noises and that the NN based approach is superior over the MA model, especially for low frequency band.

关键词： active noise control model-based signal processing neural network engine noise control traffic noise control

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INSTANTANEOUS POSE ESTIMATION USING ROTATION VECTORS

INSTANTANEOUS POSE ESTIMATION USING ROTATION VECTORS

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IEEE International Conference on Acoustics, Speech and signal processing

作者： Beutler, Frederik Huber, Marco F. Hanebeck, Uwe D. Univ Karlsruhe Intelligent Sensor Actuator Syst Lab Inst Comp Sci & Engn Karlsruhe Germany

ISBN: (纸本)9781424423538

An algorithm for estimating the pose, i.e., translation and rotation, of an e tended target ob ect is introduced. ompared to conventional methods, here pose estimation is performed on the basis of time-of-flight F measurements bet een e ternal sources and sensors attached to the ob ect, the proposed approach directly uses the amplitude values measured at the sensors for estimation purposes ithout an intermediate F estimation step. his is achieved by modeling the ave propagation by a nonlinear dynamic system comprising a system and a measurement e uation. he nonlinear system e uation includes a model of the time-variant structure of the ob ect rotation based on rotation vectors. As a result, the measured amplitude values at the sensors can be processed instantaneously in a recursive fashion. Uncertainties in the measurement process are systematically considered by employing a stochastic filter for estimating the pose, i.e., the state of the nonlinear dynamic system.

关键词： Acoustic signal processing acoustic trac ing inertial navigation model-based signal processing state estimation

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An adaptively tunable model of the cardiac signal for the bio-impedance signal decomposer (BISD)

An adaptively tunable model of the cardiac signal for the bi...

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IEEE International Workshop on Medical Measurements and Applications (MeMeA 2008)

作者： Krivoshei, Andrei Kukk, Vello Min, Mart Tallinn Univ Technol Dept Elect Ehitajate Tee 5 EE-19086 Tallinn Estonia Tallinn Uinv Techol Dept Comp Control Tallinn 19086 Estonia

ISBN: (纸本)9781424419371

The paper presents the further development of the bio-impedance signal decomposer (BISD) of the total bio-impedance (BI) signal to its cardiac and respiratory components. The Jacobi orthonormal polynomials based adaptively tunable model of the cardiac BI signal is proposed in the paper, which plays very important role in the decomposition task The importance arises from the fact, that the BISD must be reliable and have to correct operate with signals taken from different persons, and in such cases, when the cardiac BI signal of a person is changing in time. For the proposed system the reliability significantly depends on the difference between the model of the cardiac signal and the real cardiac signal (the reference signal). Tie averaged through several periods version of the already separated cardiac BI signal is used as reference signal in the proposed algorithm for tuning the parameters of the cardiac BI signal model using a modified Newton adaptation algorithm. After the model is elaborated, the system separates the cardiac and the respiratory components more accurately by tracking the cardiac BI signal.

关键词： adaptive signal processing adaptive parameter estimation bio-impedance model-based signal processing heart rate monitoring

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Decomposition method of an electrical bio-impedance signal into cardiac and respiratory components

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PHYSIOLOGICAL MEASUREMENT 2008年第6期29卷 S15-S25页

作者： Krivoshei, A. Kukk, V. Min, M. Tallinn Univ Technol Dept Elect EE-19086 Tallinn Estonia Tallinn Univ Technol Dept Comp Control EE-19086 Tallinn Estonia

The paper presents a method for adaptive decomposition of an electrical bio-impedance (BI) signal into two components: cardiac and respiratory. The decomposition of a BI signal is not a trivial process because of the non-stationarity of the signal components and overlapping of their harmonic spectra. An application specific orthonormal basis (ASOB) was designed to solve the decomposition task using the Jacobi weighting function in the standard Gram Schmidt process. The key element of the bio-impedance signal decomposer (BISD) is a model of the cardiac BI signal, which is constructed from the components of the ASOB and is intended for use in the BISD for on-line tracking of the cardiac BI signal. It makes it possible to separate the cardiac and respiratory components of the total BI signal in non-stationary conditions. In combination with the signal-shape locked loop (SSLL), the BISD allows us to decompose the BI signals with partially overlapping spectra. The proposed BISD based method is accomplished as a PC software digital system, but it is oriented towards applications in portable and stationary cardiac devices and in clinical settings.

关键词： bio-impedance independent components separation model-based signal processing heart rate monitoring cardiac pacing

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An Adaptively Tunable model of the Cardiac signal for the Bio-Impedance signal Decomposer (BISD)

An Adaptively Tunable Model of the Cardiac Signal for the Bi...

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2008 IEEE International Workshop on Medical Measurements and Applications (MeMeA 2008)

作者： Andrei Krivoshei Vello Kukk Mart Min Tallinna Tehnikaulikool Tallinn Harjumaa EE Department of Computer Control Tallinn University of Technology Tallinn Estonia Department of Electronics Tallinn University of Technology Ehitajate tee 5 19086 Tallinn Estonia

The paper presents the further development of the bio-impedance signal decomposer (BISD) of the total bio-impedance (BI) signal to its cardiac and respiratory components. The Jacobi orthonormal polynomials based adaptively tunable model of the cardiac BI signal is proposed in the paper, which plays very important role in the decomposition task. The importance arises from the fact, that the BISD must be reliable and have to correct operate with signals taken from different persons, and in such cases, when the cardiac BI signal of a person is changing in time. For the proposed system the reliability significantly depends on the difference between the model of the cardiac signal and the real cardiac signal (the reference signal). The averaged through several periods version of the already separated cardiac BI signal is used as reference signal in the proposed algorithm for tuning the parameters of the cardiac BI signal model using a modified Newton adaptation algorithm. After the model is elaborated, the system separates the cardiac and the respiratory components more accurately by tracking the cardiac BI signal.

关键词： Adaptive signal processing Adaptive parameter estimation Bio-impedance model-based signal processing Heart rate monitoring

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Varied excitation for the improving of information extraction from signals of single sensors

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TM-TECHNISCHES MESSEN 2006年第7-8期73卷 382-392页

作者： Kanoun, Olfa Universität der Bundeswehr München Germany Institut für Mess- und Automatisierungstechnik Universität der Bundeswehr München 85577 Neubiberg Germany

Different approaches can be pursued in order to improve sensory information. The approach of this work is using one single sensor element under varied excitation, such as current or frequency. The dependence of the sensor output on the excitation can be used to separate the effects according to their different reactions on the excitation. Changes in the dependence of the sensor output on excitation can be identified and utilized for self-test and self-calibration. This kind of sensor systems called Varied Input Sensor systems (VIS) closes the gap between single sensors and multi-sensor systems. The development process of this kind of sensor systems needs special care for a robust and undisturbed function.

关键词： sensor signal processing model-based signal processing control parameter impedance spectroscopy

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Effects of non-nutritive sucking on heart rate, respiration and oxygenation:: a model-based signal processing approach

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COMPARATIVE BIOCHEMISTRY AND PHYSIOLOGY A-MOLECULAR AND INTEGRATIVE PHYSIOLOGY 2002年第1期132卷 97-106页

作者： Morren, G Van Huffel, S Helon, I Naulaers, G Daniëls, H Devlieger, H Casaer, P Katholieke Univ Leuven SISTA ESQT SCDDept Elect Engn B-3001 Louvain Belgium Univ Hosp Gasthuisberg Dept Paediat B-3000 Louvain Belgium

Several studies support the idea that the use of pacifiers can reduce the risk of Sudden Infant Death Syndrome. To investigate the effect of non-nutritive sucking (NNS), we measured heart rate, abdominal respiration, EMG and arterial oxygen saturation of 20 neonates. Also, in 10 of these neonates, changes in cerebral hemoglobin concentrations were acquired by means of near-infrared spectroscopy. Using a parametric technique to model the heart rate as a sum of exponentially damped sinusoids, two main frequency components were found in the heart rate during NNS: a frequency of approximately 0.08 Hz due to the alternation of sucking bursts and pauses, and a frequency of approximately 0.8 Hz that reflects the influence of the respiration. Our analysis shows that it is the alternation of bursts and pauses itself that causes the increased heart rate variability, and that this is not due to increased effort. This suggests that the neuronal mechanism regulating NNS also stimulates the heart rate. From our measurements, no effect of NNS on cerebral or peripheral oxygenation could be found. Furthermore, we show that our model-based signal processing technique is well suited for the analysis of non-stationary biomedical signals. (C) 2002 Elsevier Science Inc. All rights reserved.

关键词： heart rate variability model-based signal processing near-infrared spectroscopy non-nutritive sucking oxygenation polysomnography respiration Sudden Infant Death Syndrome

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signal processing aspects of fusion plasma broadband reflectometry

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IEEE TRANSACTIONS ON signal processing 1999年第2期47卷 378-388页

作者： Nunes, FD Leitao, JMN Univ Tecn Lisboa Inst Telecomunicacoes EURATOM IST AssocInst Super Tecn Lisbon Portugal Univ Tecn Lisboa Ctr Fusao Nucl EURATOM IST AssocInst Super Tecn Lisbon Portugal

Microwave reflectometry is a radar technique utilized by nuclear fusion diagnostics to evaluate the plasma electron density distribution (density profile) and its local fluctuations in experimental devices, e.g., tokamaks, It exploits the fact that an electromagnetic wave launched into the plasma is reflected at the layer where the refractive index vanishes. By mixing the incident and reflected waves, a phase-modulated reflectometric signal is produced. In O-mode broadband reflectometry, the density profile is determined by sweeping the frequency of the incident wave, estimating the phase-rate of the reflectometric signal and computing its Abel inversion integral. In this paper, a stochastic nonlinear filtering approach is adopted for the estimation problem. The joint phase and phase-rate dynamics is modeled as a vector Gauss-Markov process from which only the first component is observed. A suboptimal nonlinear filter tailored to the features of the problem under study is developed and tested by simulation and applied to real data. This estimator exhibits significant advantages over the extended Kalman-Bucy filter, which is used in this work as a benchmark.

关键词： broadband reflectometry model-based signal processing phase/phase-rate estimation stochastic nonlinear filtering

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