| 1. |
- Glentis, G. -O, et al.
(author)
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Efficient spectral analysis in the missing data case using sparse ML methods
- 2014
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In: European Signal Processing Conference. - 2219-5491.
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Conference paper (peer-reviewed)abstract
- Given their wide applicability, several sparse high-resolution spectral estimation techniques and their implementation have been examined in the recent literature. In this work, we further the topic by examining a computationally efficient implementation of the recent SMLA algorithms in the missing data case. The work is an extension of our implementation for the uniformly sampled case, and offers a notable computational gain as compared to the alternative implementations in the missing data case.
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| 2. |
- Adalbjörnsson, Stefan Ingi, et al.
(author)
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High resolution sparse estimation of exponentially decaying two-dimensional signals
- 2014
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In: European Signal Processing Conference. - 2219-5491.
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Conference paper (peer-reviewed)abstract
- In this work, we consider the problem of high-resolution estimation of the parameters detailing a two-dimensional (2-D) signal consisting of an unknown number of exponentially decaying sinusoidal components. Interpreting the estimation problem as a block (or group) sparse representation problem allows the decoupling of the 2-D data structure into a sum of outer-products of 1-D damped sinusoidal signals with unknown damping and frequency. The resulting non-zero blocks will represent each of the 1-D damped sinusoids, which may then be used as non-parametric estimates of the corresponding 1-D signals; this implies that the sought 2-D modes may be estimated using a sequence of 1-D optimization problems. The resulting sparse representation problem is solved using an iterative ADMM-based algorithm, after which the damping and frequency parameter can be estimated by a sequence of simple 1-D optimization problems.
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| 3. |
- Adalbjörnsson, Stefan Ingi, et al.
(author)
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Sparse Estimation Of Spectroscopic Signals
- 2011
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In: European Signal Processing Conference. - 2219-5491. ; 2011, s. 333-337
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Conference paper (peer-reviewed)abstract
- This work considers the semi-parametric estimation of sparse spec- troscopic signals, aiming to form a detailed spectral representation of both the frequency content and the spectral line widths of the oc- curring signals. Extending on the recent FOCUSS-based SLIM al- gorithm, we propose an alternative prior for a Bayesian formulation of this sparse reconstruction method, exploiting a proposed suitable prior for the noise variance. Examining three common models for spectroscopic signals, the introduced technique allows for reliable estimation of the characteristics of these models. Numerical sim- ulations illustrate the improved performance of the proposed tech- nique.
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| 4. |
- Angelopoulos, Kostas, et al.
(author)
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Efficient Time Recursive Coherence Spectrum Estimation
- 2012
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In: Signal Processing Conference (EUSIPCO), 2012 Proceedings of the 20th European. - 2076-1465 .- 2219-5491. - 9781467310680 ; , s. 425-429
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Conference paper (peer-reviewed)abstract
- The coherence spectrum is of notable interest as a bivariate spectral measure in a variety of application, and the topic has lately attracted notable interest with the recent formulation of several high-resolution data adaptive estimators. In this work, we present computationally efficient time recursive implementations of the recent iterative adaptive approach (IAA) estimator, examining both the case of complete data sets and when some observations are missing. The algorithms continues the recent development of exploiting the estimators’ inherently low displacement rank of the necessary products of Toeplitz-like matrices, extending these to time-updating formulations for the IAA-based coherence estimation algorithm. Numerical simulations together with theoretical complexity measures illustrate the performance of the proposed algorithm.
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| 5. |
- Ask, Erik, et al.
(author)
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A Unifying Approach to Minimal Problems in Collinear and Planar TDOA Sensor Network Self-Calibration
- 2014
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In: European Signal Processing Conference. - 2219-5491.
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Conference paper (peer-reviewed)abstract
- This work presents a study of sensor network calibration from time-difference-of-arrival (TDOA) measurements for cases when the dimensions spanned by the receivers and the transmitters differ. This could for example be if receivers are restricted to a line or plane or if the transmitting objects are moving linearly in space. Such calibration arises in several applications such as calibration of (acoustic or ultrasound) microphone arrays, and radio antenna networks. We propose a non-iterative algorithm based on recent stratified approaches: (i) rank constraints on modified measurement matrix, (ii) factorization techniques that determine transmitters and receivers up to unknown affine transformation and (iii) determining the affine stratification using remaining non-linear constraints. This results in a unified approach to solve almost all minimal problems. Such algorithms are important components for systems for self-localization. Experiments are shown both for simulated and real data with promising results.
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| 6. |
- Björk, Marcus, 1985-, et al.
(author)
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Signal Processing Algorithms for Removing Banding Artifacts in MRI
- 2011
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In: Proceedings of the 19th European Signal Processing Conference (EUSIPCO-2011). ; , s. 1000-1004, s. 1000-1004
-
Conference paper (peer-reviewed)abstract
- In magnetic resonance imaging (MRI), the balanced steady-state free precession (bSSFP) pulse sequence has shown to be of great interest, due to its relatively high signal-to-noise ratio in a short scan time. However, images acquired with this pulse sequence suffer from banding artifacts due to off-resonance effects. These artifacts typically appear as black bands covering parts of the image and they severely degrade the image quality. In this paper, we present a fast two-step algorithm for estimating the unknowns in the signal model and removing the banding artifacts. The first step consists of rewriting the model in such a way that it becomes linear in the unknowns (this step is named Linearization for Off-Resonance Estimation, or LORE). In the second step, we use a Gauss-Newton iterative optimization with the parameters obtained by LORE as initial guesses. We name the full algorithm LORE-GN. Using both simulated and in vivo data, we show the performance gain associated with using LORE-GN as compared to general methods commonly employed in similar cases.
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| 7. |
- Brynolfsson, Johan, et al.
(author)
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Multitaper Estimation of the Coherence Spectrum in low SNR
- 2014
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In: European Signal Processing Conference. - 2219-5491.
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Conference paper (peer-reviewed)abstract
- A pseudo coherence estimate using multitapers is presented. The estimate has better localization for sinusoids and is shown to have lower variance for disturbances compared to the usual coherence estimator. This makes it superior in terms of finding coherent frequencies between two sinusoidal signals; even when observed in low SNR. Different sets of multitapers are investigated and the weights of the final coherence estimate are adjusted for a low-biased estimate of a single sinusoid. The proposed method is more computationally efficient than data dependent methods, and does still give comparable results.
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| 8. |
- Garcia, Guillermo, 1976, et al.
(author)
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Sample iterative likelihood maximization for speaker verification systems
- 2010
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In: 18th European Signal Processing Conference, EUSIPCO 2010; Aalborg; Denmark; 23 August 2010 through 27 August 2010. - 2219-5491. ; , s. 596-600
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Conference paper (peer-reviewed)abstract
- Gaussian Mixture Models (GMMs) have been the dominant technique used for modeling in speaker recognition systems. Traditionally, the GMMs are trained using the Expectation Maximization (EM) algorithm and a large set of training samples. However, the convergence of the EM algorithm to a global maximum is conditioned on proper parameter initialization, a large enough training sample set, and several iterations over this training set. In this work, a Sample Iterative Likelihood Maximization (SILM) algorithm based on a stochastic descent gradient method is proposed. Simulation results showed that our algorithm can attain high loglikelihoods with fewer iterations in comparison to the EMalgorithm. A maximum of eight times faster convergence rate can be achieved in comparison with the EM algorithm.
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| 9. |
- Garcia, Guillermo, 1976, et al.
(author)
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Study of mutual information for speaker recognition features
- 2010
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In: 18th European Signal Processing Conference, EUSIPCO 2010; Aalborg; Denmark; 23 August 2010 through 27 August 2010. - 2219-5491. ; , s. 601-605
-
Conference paper (peer-reviewed)abstract
- Feature extraction is an important stage in speaker recognition systems since the overall performance depends on the type of the extracted features. In the framework of speaker recognition, the extracted features are mainly based on transformations of the speech spectrum. In spite of the great variety of features extracted from the speech, the common empirical approach to select features is based on a complete performance evaluation of the system. In this paper, we propose an information theory approach to evaluate the information about the speaker identity contained on the speech features. The results show that this approach can help on a more efficient feature selection. We also present an alternative AMFMbased magnitude representation of the speech that attains better performance than the MFCCs. Moreover, we show that phase information features can perform well in speaker verification systems.
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| 10. |
- Glentis, George-Othan, et al.
(author)
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Preconditioned Conjugate Gradient IAA Spectral Estimation
- 2011
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In: European Signal Processing Conference. - 2219-5491. ; 2001, s. 1195-1199
-
Conference paper (peer-reviewed)abstract
- In this paper, we develop superfast approximative algorithms for the computationally efficient implementation of the recent Iterative Adaptive Approach (IAA) spectral estimate. The proposed methods are based on rewriting the IAA algorithm using suitable Gohberg-Semencul representations, solving the resulting linear systems of equations using the preconditioned conjugate gradient method, where a novel preconditioning is applied using an incomplete factorization of the Toeplitz matrix. Numerical simulations illustrate the efficiency of the proposed algorithm.
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