The Experts below are selected from a list of 15828 Experts worldwide ranked by ideXlab platform
J K Tugnait - One of the best experts on this subject based on the ideXlab platform.
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blind detection of asynchronous cdma signals in multipath channels using code constrained Inverse Filter criterion
IEEE Transactions on Signal Processing, 2001Co-Authors: J K TugnaitAbstract:A code-constrained Inverse Filter criterion based approach is presented for blind detection of asynchronous short-code direct sequence code division multiple access (DS-CDMA) signals in multipath channels. Only the spreading code of the desired user is assumed to be known; its transmission delay may be unknown. We focus on maximization of the normalized fourth cumulant of Inverse Filtered (equalized) data with respect to (w.r.t.) the equalizer coefficients subject to the equalizer lying in a subspace associated with the desired user's code sequence. Constrained maximization leads to extraction of the desired user's signal, whereas unconstrained maximization leads to the extraction of any one of the active users. Illustrative simulation examples are provided.
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further results on blind asynchronous cdma receivers using code constrained Inverse Filter criterion
International Conference on Acoustics Speech and Signal Processing, 2001Co-Authors: J K TugnaitAbstract:A code-constrained Inverse Filter criterion (CC-IFC) based approach was presented Tugnait and Li (see Proc. IEEE 2000 ICASSP, p.V-246-64, Istanbul, Turkey, June 2000) for blind-detection of asynchronous short-code DS-CDMA (direct sequence code division multiple access) signals in multipath channels. Only the spreading code of the desired user is assumed to be known; its transmission delay may be unknown. The equalizer was determined by maximizing the magnitude of the normalized fourth cumulant of Inverse Filtered (equalized) data with respect to the equalizer coefficients subject to the fact that the equalizer lies in a subspace associated with the desired user's code sequence. In this paper we analyze the identifiability properties of the approach of Tugnait and Li. Global maxima and some of the local maxima of the cost function are investigated. These aspects were not discussed by Tugnait and Li. More extensive simulation comparisons with existing approaches are also provided.
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identification and deconvolution of multichannel linear non gaussian processes using higher order statistics and Inverse Filter criteria
IEEE Transactions on Signal Processing, 1997Co-Authors: J K TugnaitAbstract:This paper is concerned with the problem of estimation and deconvolution of the matrix impulse response function of a multiple-input multiple-output (MIMO) system given only the measurements of the vector output of the system. The system is assumed to be driven by a temporally i.i.d. and spatially independent non-Gaussian vector sequence (which is not observed). An iterative, Inverse Filter criteria-based approach is developed using the third-order or the fourth-order normalized cumulants of the Inverse Filtered data at zero lag. Stationary points of the proposed cost functions are investigated. The approach is input iterative, i.e., the input sequences are extracted and removed one by one. The matrix impulse response is then obtained by cross correlating the extracted inputs with the observed outputs. Identifiability conditions are analyzed. The strong consistency of the proposed approach is also briefly discussed. Computer simulation examples are presented to illustrate the proposed approaches.
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estimation of linear parametric models using Inverse Filter criteria and higher order statistics
IEEE Transactions on Signal Processing, 1993Co-Authors: J K TugnaitAbstract:Considers the problem of estimating the parameters of a stable, scalar ARMA (p, q) signal model (causal or noncausal, minimum phase or mixed phase) driven by an i.i.d. non-Gaussian sequence. The driving noise sequence is not observed. The Wiggins-Donoho (1978, 1991) class of Inverse Filter criteria for estimation of model parameters are analyzed and extended. These criteria have been considered in the past only for moving average Inverse Filters. These criteria are extended to general ARMA Inverses. Computer simulation examples are presented to illustrate the proposed approaches. >
Masato Miyoshi - One of the best experts on this subject based on the ideXlab platform.
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Inverse Filtering for speech dereverberation less sensitive to noise and room transfer function fluctuations
EURASIP Journal on Advances in Signal Processing, 2007Co-Authors: Takafumi Hikichi, Marc Delcroix, Masato MiyoshiAbstract:Inverse Filtering of room transfer functions (RTFs) is considered an attractive approach for speech dereverberation given that the time invariance assumption of the used RTFs holds. However, in a realistic environment, this assumption is not necessarily guaranteed, and the performance is degraded because the RTFs fluctuate over time and the Inverse Filter fails to remove the effect of the RTFs. The Inverse Filter may amplify a small fluctuation in the RTFs and may cause large distortions in the Filter's output. Moreover, when interference noise is present at the microphones, the Filter may also amplify the noise. This paper proposes a design strategy for the Inverse Filter that is less sensitive to such disturbances. We consider that reducing the Filter energy is the key to making the Filter less sensitive to the disturbances. Using this idea as a basis, we focus on the influence of three design parameters on the Filter energy and the performance, namely, the regularization parameter, modeling delay, and Filter length. By adjusting these three design parameters, we confirm that the performance can be improved in the presence of RTF fluctuations and interference noise.
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on robust Inverse Filter design for room transfer function fluctuations
European Signal Processing Conference, 2006Co-Authors: Takafumi Hikichi, Marc Delcroix, Masato MiyoshiAbstract:Dereverberation methods based on the Inverse Filtering of room transfer functions (RTFs) are attractive because high deconvolution performance can be achieved. Although many methods assume that the RTFs are time-invariant, this assumption would not be guaranteed in practice. This paper deals with the problem of the sensitivity of a dereverberation algorithm based on Inverse Filtering. We evaluate the effect of RTF fluctuations caused by source position changes on the dereverberation performance. We focus on the Filter energy with a view to making the Filter less sensitive as regards these fluctuations. By adjusting three design parameters, namely, Filter length, modeling delay, and regularization parameter, a dereverberation performance of up to 15 dB of the signal-to-distortion ratio could be obtained when the source position was changed with one-eighth wavelength distance, whereas conventional investigations have claimed that such a variation would cause a large degradation.
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robust decomposition of Inverse Filter of channel and prediction error Filter of speech signal for dereverberation
European Signal Processing Conference, 2006Co-Authors: Takuya Yoshioka, Masato Miyoshi, Takafumi Hikichi, Hiroshi G OkunoAbstract:This paper estimates the Inverse Filter of a signal transmission channel of a room driven by a speech signal. Speech signals are often modeled as piecewise stationary autoregressive (AR) processes. The most fundamental issue is how to estimate a channel's Inverse Filter separately from the Inverse Filter of the speech generating AR system, or the prediction error Filter (PEF). We first point out that by jointly estimating the channel's Inverse Filter and the PEF, the channel's Inverse is identifiable due to the time varying nature of the PEF. Then, we develop an algorithm that achieves this joint estimation. The notable property of the proposed method is its robustness against deviation from the linear convolutive model of an observed signal caused by, for example, observation noise. Experimental results with simulated and real recorded reverberant signals showed the effectiveness of the proposed method.
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speech dereverberation algorithm using transfer function estimates with overestimated order
Acoustical Science and Technology, 2006Co-Authors: Takafumi Hikichi, Masato Miyoshi, Marc DelcroixAbstract:This paper addresses the blind dereverberation problem of a single-input multiple-output acoustic system. Many conventional approaches require a precise order of the transfer functions. In this paper, we propose an equalization algorithm that is less sensitive to the order misadjustment of the transfer functions. First, the transfer functions are estimated using an overestimated order, and the Inverse Filter set for these estimated transfer functions is calculated. Since the estimated transfer functions contain a common polynomial, the signal processed by the Inverse Filter set suffers from the effect of this common polynomial. Then, we extract this polynomial to compensate for the distortion. The proposed algorithm recovers input signal as long as the channel is overestimated. Simulation results show that the proposed method works well even when the order is highly overestimated.
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fast estimation of a precise dereverberation Filter based on speech harmonicity
International Conference on Acoustics Speech and Signal Processing, 2005Co-Authors: Keisuke Kinoshita, Tomohiro Nakatani, Masato MiyoshiAbstract:A speech signal captured by a distant microphone is generally smeared by reverberation. This severely degrades both the speech intelligibility and automatic speech recognition (ASR) performance. In this paper, we propose a new dereverberation scheme based on harmonicity based dereverberation (HERB), aiming primarily at reducing the amount of training data needed to estimate an Inverse Filter. We show experimentally that our new dereverberation scheme successfully achieves high quality dereverberation with much smaller amounts of training data, and is very effective at improving both audible quality and ASR performance, even in unknown severely reverberant environments.
Chongyung Chi - One of the best experts on this subject based on the ideXlab platform.
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blind mai and isi suppression for ds cdma systems using hos based Inverse Filter criteria
IEEE Transactions on Signal Processing, 2002Co-Authors: Chongyung Chi, Chihorng Chen, Chingyung ChenAbstract:Cumulant-based Inverse Filter criteria (IFC) using second-and higher order statistics (HOS) proposed by Tugnait et al. (1993) have been widely used for blind deconvolution of discrete-time multi-input multi-output (MIMO) linear time-invariant systems with non-Gaussian measurements through a multistage successive cancellation procedure, but the deconvolved signals turn out to be an unknown permutation of the driving inputs. A multistage blind equalization algorithm (MBEA) is proposed for multiple access interference (MAI) and intersymbol interference (ISI) suppression of multiuser direct sequence/code division multiple access (DS/CDMA) systems in the presence of multipath. The proposed MBEA, which processes the chip waveform matched Filter output signal without requiring any path delay information, includes blind deconvolution processing using IFC followed by identification of the estimated symbol sequence with the associated user through a user identification algorithm (UIA). Then, some simulation results are presented to support the proposed MBEA and UIA. Finally, some conclusions are drawn.
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cumulant based Inverse Filter criteria for mimo blind deconvolution properties algorithms and application to ds cdma systems in multipath
IEEE Transactions on Signal Processing, 2001Co-Authors: Chongyung Chi, Chiihorng ChenAbstract:Tugnait (1995) and Chi and Chen proposed multi-input multi-output Inverse Filter criteria (MIMO-IFC) using higher order statistics for blind deconvolution of MIMO linear time-invariant systems. This paper proposes three properties on the performance of the MIMO linear equalizer associated with MIMO-IFC for any signal-to-noise ratio, including (P1) perfect phase equalization property, (P2) a relation to MIMO minimum mean square error (MIMO-MMSE) equalizer, and (P3) a connection with the one obtained by MIMO super-exponential algorithm (MIMO-SEA) that usually converges fast but does not guarantee convergence for finite data. Based on (P2), a fast algorithm for computing the theoretically optimum MIMO equalizer is proposed. Moreover, based on (P3), a fast MIMO-IFC based algorithm with performance similar to that of the MIMO-SEA and with guaranteed convergence is proposed as well as its application to suppression of multiple access interference and intersymbol interference (ISI) for multiuser asynchronous DS/CDMA systems in multipath. Finally, some simulation results are presented to support the analytic results and the proposed algorithms.
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Inverse Filter criteria for blind deconvolution and equalization using two cumulants
Signal Processing, 1995Co-Authors: Chongyung ChiAbstract:Abstract Cumulant (higher-order statistics) based Inverse Filter criteria maximizing J r,m = ¦C m ¦ r ¦C r ¦ m , where m ≠ r and Cm (Cr) denotes the mth-order (rth-order) cumulant of the Inverse Filter output, have been proposed for blind deconvolution and equalization with only non-Gaussian output measurements of an unknown linear time-invariant (LTI) system. This paper shows that the maximum of Jr,m, associated with the true Inverse Filter of the unknown LTI system, exists only for r to be even and m > r, otherwise Jr,m is unbounded. The admissible values for (r, m) = (2s, l + s) where l > s ⩾ 1 include (2, 3), (2, 4) and (4, 6) proposed by Tugnait, Wiggins, Shalvi and Weinstein in addition to the new ones such as (2, 5), (2, 6) and (4, 5). Some simulation results associated with the Inverse Filter criteria Jr,m with the admissible values for (r, m) are then provided. Finally, we draw some conclusions.
Yoram Bresler - One of the best experts on this subject based on the ideXlab platform.
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multichannel sparse blind deconvolution on the sphere
IEEE Transactions on Information Theory, 2019Co-Authors: Yoram BreslerAbstract:Multichannel blind deconvolution is the problem of recovering an unknown signal $f$ and multiple unknown channels $x_{i}$ from their circular convolution $y_{i}=x_{i} \circledast f$ ( $i=1,2, {\dots },N$ ). We consider the case where the $x_{i}$ ’s are sparse, and convolution with $f$ is invertible. Our nonconvex optimization formulation solves for a Filter $h$ on the unit sphere that produces sparse output $y_{i}\circledast h$ . Under some technical assumptions, we show that all local minima of the objective function correspond to the Inverse Filter of $f$ up to an inherent sign and shift ambiguity, and all saddle points have strictly negative curvatures. This geometric structure allows successful recovery of $f$ and $x_{i}$ using a simple manifold gradient descent (MGD) algorithm. The same approach is also applicable to blind gain and phase calibration with a Fourier sensing matrix. Our algorithm and analysis require fewer assumptions than previous algorithms for the same problem. Our theoretical findings are complemented by numerical experiments, which demonstrate superior performance of the proposed approach over the previous methods. Empirically, our algorithm has low computation cost (converging in a small number of iterations) and low memory footprint (solving only for the Inverse Filter of $f$ ).
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multichannel sparse blind deconvolution on the sphere
International Conference on Acoustics Speech and Signal Processing, 2019Co-Authors: Yoram BreslerAbstract:Multichannel blind deconvolution is the problem of recovering an unknown signal f and multiple unknown channels x i from convolutional measurements y i = x i ⊛ f (i = 1, 2, …, N). We consider the case where the x i ’s are sparse, and convolution with f is invertible. Our nonconvex optimization formulation solves for a Filter h on the unit sphere that produces sparse output y i ⊛ h. Under some technical assumptions, we show that all local minima of the objective function correspond to the Inverse Filter of f up to an inherent sign and shift ambiguity, and all saddle points have strictly negative curvatures. This geometric structure allows successful recovery of f and x i using a simple manifold gradient descent algorithm with random initialization. Our theoretical findings are complemented by numerical experiments, which demonstrate superior performance of the proposed approach over the previous methods.
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global geometry of multichannel sparse blind deconvolution on the sphere
2018Co-Authors: Yoram BreslerAbstract:Multichannel blind deconvolution is the problem of recovering an unknown signal $f$ and multiple unknown channels $x_i$ from their circular convolution $y_i=x_i \circledast f$ ($i=1,2,\dots,N$). We consider the case where the $x_i$'s are sparse, and convolution with $f$ is invertible. Our nonconvex optimization formulation solves for a Filter $h$ on the unit sphere that produces sparse output $y_i\circledast h$. Under some technical assumptions, we show that all local minima of the objective function correspond to the Inverse Filter of $f$ up to an inherent sign and shift ambiguity, and all saddle points have strictly negative curvatures. This geometric structure allows successful recovery of $f$ and $x_i$ using a simple manifold gradient descent (MGD) algorithm. Our theoretical findings are complemented by numerical experiments, which demonstrate superior performance of the proposed approach over the previous methods.
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global geometry of multichannel sparse blind deconvolution on the sphere
Neural Information Processing Systems, 2018Co-Authors: Yoram BreslerAbstract:Multichannel blind deconvolution is the problem of recovering an unknown signal $f$ and multiple unknown channels $x_i$ from convolutional measurements $y_i=x_i \circledast f$ ($i=1,2,\dots,N$). We consider the case where the $x_i$'s are sparse, and convolution with $f$ is invertible. Our nonconvex optimization formulation solves for a Filter $h$ on the unit sphere that produces sparse output $y_i\circledast h$. Under some technical assumptions, we show that all local minima of the objective function correspond to the Inverse Filter of $f$ up to an inherent sign and shift ambiguity, and all saddle points have strictly negative curvatures. This geometric structure allows successful recovery of $f$ and $x_i$ using a simple manifold gradient descent algorithm with random initialization. Our theoretical findings are complemented by numerical experiments, which demonstrate superior performance of the proposed approach over the previous methods.
Mathias Fink - One of the best experts on this subject based on the ideXlab platform.
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real time Inverse Filter focusing through iterative time reversal
Journal of the Acoustical Society of America, 2004Co-Authors: Gabriel Montaldo, Mickael Tanter, Mathias FinkAbstract:In order to achieve an optimal focusing through heterogeneous media we need to build the Inverse Filter of the propagation operator. Time reversal is an easy and robust way to achieve such an Inverse Filter in nondissipative media. However, as soon as losses appear in the medium, time reversal is not equivalent to the Inverse Filter anymore. Consequently, it does not produce the optimal focusing and beam degradations may appear. In such cases, we showed in previous works that the optimal focusing can be recovered by using the so-called spatiotemporal Inverse Filter technique. This process requires the presence of a complete set of receivers inside the medium. It allows one to reach the optimal focusing even in extreme situations such as ultrasonic focusing through human skull or audible sound focusing in strongly reverberant rooms. But, this technique is time consuming and implied fastidious numerical calculations. In this paper we propose a new way to process this Inverse Filter focusing technique in real time and without any calculation. The new process is based on iterative time reversal process. Contrary to the classical Inverse Filter technique, this iteration does not require any computation and achieves the Inverse Filter in an experimental way using wave propagation instead of computational power. The convergence from time reversal to Inverse Filter during the iterative process is theoretically explained. Finally, the feasibility of this iterative technique is experimentally demonstrated for ultrasound applications.
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Sound focusing in rooms. II. The spatio-temporal Inverse Filter.
The Journal of the Acoustical Society of America, 2003Co-Authors: Sylvain Yon, Mickael Tanter, Mathias FinkAbstract:The potential of time reversal processing for room acoustics has been extensively investigated in the companion of this paper [J. Acoust. Soc. Am. 113(3), 1533-1543 (2003)]. In particular, a simple implementation of a loudspeaker time reversal antenna able to take advantage of the multiple reflections in reverberating rooms demonstrates its potential for audible range acoustics while improving focusing both in space and time. However, loss of information (e.g., sound absorption in walls or nonequalized bandwidths of the loudspeakers) during a time reversal experiment degrades the quality of time reversal focusing. In this paper, a more sophisticated technique called spatio-temporal Inverse Filtering is investigated that achieves time and space deconvolution of the propagation operator between the loudspeakers antenna and a set of microphones embedded inside the insonified volume. Theoretical and experimental comparisons between time reversal and Inverse Filter focusing are presented. Finally, advantages and limitations of both focusing approaches are highlighted.
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optimal focusing by spatio temporal Inverse Filter ii experiments application to focusing through absorbing and reverberating media
Journal of the Acoustical Society of America, 2001Co-Authors: Jeanfrancois Aubry, J. L. Thomas, Mickael Tanter, James S Gerber, Mathias FinkAbstract:To focus ultrasonic waves in an unknown heterogeneous medium using a phased array, one has to calculate the optimal set of signals to be applied on the transducers of the array. (In most applications of ultrasound, medical imaging, medical therapy, nondestructive testing, the first step consists of focusing a broadband ultrasound beam deeply inside the medium to be investigated.) Focusing in a homogeneous medium simply requires to compensate for the varying focus–array elements geometrical distances. Nevertheless, heterogeneities in the medium, in terms of speed of sound, density, or absorption, may strongly degrade the focusing. Different techniques have been developed in order to correct such aberrations induced by heterogeneous media (time reversal, speckle brightness, for example). In the companion to this paper, a new broadband focusing technique was investigated: the spatio-temporal Inverse Filter. Experimental results obtained in various media, such as reverberating and absorbing media, are present...
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Optimal focusing by spatio-temporal Inverse Filter. I. Basic principles
2001Co-Authors: Mickael Tanter, Jean Fra??ois Aubry, J. Gerber, J. L. Thomas, Mathias FinkAbstract:A focusing technique based on the inversion of the propagation operator\nrelating an array of transducers to a set of control points inside\na medium was proposed in previous work [Tanter et al., J. Acoust.\nSoc. Am. 108, 223-234 (2000)] and is extended here to the time domain.\nAs the inversion of the propagation operator is achieved both in\nspace and time, this technique allows calculation of the set of temporal\nsignals to be emitted by each element of the array in order to optimally\nfocus on a chosen control point. This broadband inversion process\ntakes advantage of the singular-value decomposition of the propagation\noperator in the Fourier domain. The physical meaning of this decomposition\nis explained in a homogeneous medium. In particular, a definition\nof the number of degrees of freedom necessary to define the acoustic\nfield generated by an array of limited aperture in a focal plane\nof limited extent is given. This number corresponds to the number\nof independent signals that can be created in the focal area both\nin space and time. In this paper, this broadband Inverse-focusing\ntechnique is compared in homogeneous media with the classical focusing\nachieved by simple geometrical considerations but also with time-reversal\nfocusing. It is shown that, even in a simple medium, slight differences\nappear between these three focusing strategies. In the companion\npaper [Aubry et al., J. Acoust. Soc. Am. 110, 48-58 (2001)] the three\nfocusing techniques are compared in heterogeneous, absorbing, or\ncomplex media where classical focusing is strongly degraded. The\nstrong improvement achieved by the spatio-temporal Inverse-Filter\ntechnique emphasizes the great potential of multiple-channel systems\nhaving the ability to apply completely different signal waveforms\non each transducer of the array. The application of this focusing\ntechnique could be of great interest in various ultrasonic fields\nsuch as medical imaging, nondestructive testing, and underwater acoustics.
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Time reversal and the Inverse Filter.
The Journal of the Acoustical Society of America, 2000Co-Authors: Mickael Tanter, J. L. Thomas, Mathias FinkAbstract:To focus ultrasonic waves in an unknown inhomogeneous medium using a phased array, one has to calculate the optimal set of signals to be applied on the transducers of the array. In the case of time-reversal mirrors, one assumes that a source is available at the focus, providing the Green's function of this point. In this paper, the robustness of this time-reversal method is investigated when loss of information breaks the time-reversal invariance. It arises in dissipative media or when the field radiated by the source is not entirely measured by the limited aperture of a time-reversal mirror. However, in both cases, linearity and reciprocity relations ensure time reversal to achieve a spatiotemporal matched Filtering. Nevertheless, though it provides robustness to this method, no constraints are imposed on the field out of the focus and sidelobes may appear. Another approach consists of measuring the Green's functions associated to the focus but also to neighboring points. Thus, the whole information characterizing the medium is known and the Inverse source problem can be solved. A matrix formalism of the propagation operator is introduced to compare the time-reversal and Inverse Filter techniques. Moreover, experiments investigated in various media are presented to illustrate this comparison.
