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We propose a method for optimizing an acoustic feature extractor for anomalous sound detection (ASD). Most ASD systems adopt outlier-detection techniques because it is difficult to collect a massive amount of anomalous sound data. To improve the performance of such outlier-detection-based ASD, it is essential to extract a set of efficient acoustic features that is suitable for identifying anomalous...
Current batch tensor methods often struggle to keep up with fast-arriving data. Even storing the full tensors that have to be decomposed can be problematic. To alleviate these limitations, tensor updating methods modify a tensor decomposition using efficient updates instead of recomputing the entire decomposition when new data becomes available. In this paper, the structure of the decomposition is...
Joint detection and estimation is an important yet little-studied problem that arises in many signal processing applications. In this paper, a sequential and robust solution approach is presented. To design the test fulfilling constraints on the error probabilities and the quality of the estimate, the problem is converted into an unconstrained form and subsequently solved using Linear Programming...
In this paper, a new Markov random field-based mixture model, where each of its components is a mixture of Student's-t and Rayleigh distributions, is proposed for clustering fMRI time-series. By introducing the non-symmetric Rayleigh distribution, the proposed algorithm has flexibility to fit various types of observed time-series. Moreover, our method incorporates Markov random field so that the spatial...
Multiplicative updates are widely used for nonnegative matrix factorization (NMF) as an efficient computational method. In this paper, we consider a class of constrained optimization problems in which a polynomial function of the product of two matrices is minimized subject to the nonnegativity constraints. These problems are closely related to NMF because the polynomial function covers many error...
Nonnegative matrix factorization (NMF) has been increasingly investigated for data analysis and dimension-reduction. To tackle large-scale data, several online techniques for NMF have been introduced recently. So far, the online NMF has been limited to the linear model. This paper develops an online version of the nonlinear kernel-based NMF, where the decomposition is performed in the feature space...
Although the field of Brain-Computer Interfacing (BCI) has made incredible advances in the last decade, current BCIs are still scarcely used outside laboratories. One reason is the lack of robustness to noise, artifacts and nonstationarity which are intrinsic parts of the recorded brain signal. Furthermore out-of-lab environments imply the presence of external variables that are largely beyond the...
The LASSO (Least Absolute Shrinkage and Selection Operator) has been a popular technique for simultaneous linear regression estimation and variable selection. Robust approaches for LASSO are needed in the case of heavy-tailed errors or severe outliers. We propose a novel robust LASSO method that has a non-parametric flavor: it solves a criterion function based on ranks of the residuals with LASSO...
Recently it has been shown that using appropriate sampling kernel, finite rate of innovation signals can be perfectly recon structed even tough they are non-bandlimited. In the presence of noise, reconstruction is achieved by an estimation procedure of all the parameters of the incoming signal. In this paper we consider the estimation of a finite stream of pulses using the Sum of Sincs (SoS) kernel...
In this paper, we tackle the problem of adapting a set of classic sparsity-inducing methods to cases when the gradient of the objective function is either difficult or very expensive to compute. Our contributions are two-fold: first, we propose methodologies for computing fair estimations of inexact gradients, second we propose novel stopping criteria for computing these gradients. For each contribution...
Buried object localization in presence of sensor phase errors is presented in this study. Phase errors are introduced by the moving of the sensor array from their original positions during the experiment which gives an erroneous object localization. To solve the problem, an objective function is defined using the exact solution of the scattered acoustic field in the MUSIC method. The DIRECT algorithm...
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