Sparse template-based adaptive filtering

Significance index related to Student's t-test

The phenomenon arises in several real-life signal processing contexts: acoustic echo-cancellation (AEC) in sound and speech,  non-destructive testing where transmitted waves may rebound at material interfaces (e.g. ultrasounds), or pattern matching in images. Here in seismic reflection or seismology. Weak signals (of interest) are buried under both strong random and structured noise. Provided appropriate templates are obtained, we propose a structured-pattern filtering algorithm (called Ricochet) through constrained adaptive filtering in a  transformed domain. Its generic methodology impose sparsity: in different wavelet frames (Haar, Daubechies, Symmlets) coefficients, using the L-1 or Manhattan norm, as well as on adaptive filter coefficients using concentration measures (for sparser filters in the time domain): L-1, the Frobenius norm squared, and the mixed L-1,2 norms). Regularity properties are constrained as well, for instance slow variation on the adaptive filter coefficients (uniform, Chebychev or L-infinity norm). Quantitative results are given with a significance index, reminiscent of the Student t-test.

A Primal-Dual Proximal Algorithm for Sparse Template-Based Adaptive Filtering: Application to Seismic Multiple Removal [doi+hal+arxiv]

Abstract: Unveiling meaningful geophysical information from seismic data requires to deal with both random and structured ``noises''. As their amplitude may be greater than signals of interest

Lost in multiples: a creeping primary (flat, bootom-right)

(primaries), additional prior information is especially important in performing efficient signal separation. We address here the problem of multiple reflections, caused by  wave-field bouncing between layers. Since only approximate models of these phenomena are available, we propose a flexible framework for time-varying adaptive filtering of seismic signals, using sparse representations,  based on inaccurate templates. We recast the joint estimation of adaptive filters and primaries in a new convex variational formulation. This approach allows us to incorporate plausible knowledge about noise statistics, data sparsity and slow filter variation in parsimony-promoting wavelet frames.  The designed primal-dual algorithm solves a  constrained  minimization problem that alleviates standard regularization issues in finding hyper-parameters. The approach demonstrates  significantly good performance in low signal-to-noise ratio conditions, both for simulated and real field seismic data.

All the metrics here are convex. Wait a bit for something completely different with non-convex penalties, namely smoothed versions of the ratio of the L1 norm over the L2 norm: Euclid in a Taxicab: Sparse Blind Deconvolution with Smoothed $\ell_1/ell_2$ Regularization, covered in Nuit Blanche, and with arxiv page and pdf.

Postdoc position: Very large data management in Geosciences

Geophysical mesh at two resolutions

[UPDATE: 2014/09/21 Position filled]

So we (IFPEN) have a postdoc position on "Very large data management in Geosciences" (gestion des très gros volumes de données en géosciences), with details at: 

• http://www.laurent-duval.eu/siva-2014-postdoc-large-data-geosciences.html

• http://www.gdr-isis.fr/news/2392/121/Postdoc-position-Very-large-data-management-in-Geosciences.html

Abstract: The main purpose of the post-doctoral work is to propose new data compression techniques for volumetric meshes able to manage seismic data values attached to geometry elements (nodes or cells) with adaptive decompression for post-processing functionalities (visualization). Compression algorithms adapted to "big data" will enable our current software scalability, for instance, geoscience fluid-flow simulation or transport combustion simulation on very large meshes. Obtained results are intended to contribute to IFPEN scientific lock about very large data management with a target of being able to process billion of cells or data samples. Results will also be used to propose new software solutions for the storage, the transfer and the processing (exploration, visualization) of these large data sets.

Résumé : L'objectif de ce post-doctorat est de proposer de nouvelles méthodes de compression de données

Seismic data compression and denoising

et de maillages volumiques capables de gérer des propriétés attachées à la géométrique (connexité de mailles, groupes spatiaux de traces sismiques), éventuellement évolutives, tout en permettant une décompression progressive et adaptée à la visualisation et au traitement. Les algorithmes de compression pour les données volumiques permettraient de les exploiter dans les outils logiciels qui manipulent des ensembles volumineux (simulation d'écoulement poreux en géosciences ou simulation de combustion en transport). Les résultats obtenus auront vocation à contribuer au verrou technologique concernant les très gros volumes de données avec une cible fixée sur le milliard de cellules ou d'échantillons. Les résultats seront notamment exploités pour assurer le stockage, le transfert mais aussi la manipulation (exploration, visualisation) de ces très gros volumes.

Computational Harmonic Analysis: Winter School

This message was communicated to me by Caroline Chaux, to share:

Computational Harmonic Analysis: Winter School, Marseille, October 2014

We are pleased to announce the winter school on Computational Harmonic Analysis - with Applications to Signal and Image Processing, that will be held in October 2014 (20-24), in Marseille, France (at CIRM).

The topics will be:

• Mathematical and numerical aspects of frame theory
• Time-frequency frames and applications to audio analysis
• Wavelets, shearlets and geometric frames (and others *-lets or directional wavelets)
• Inverse problems and optimization

This winter school will bring together PhD-students and young PostDocs (as well as a few experts) in the field of computational harmonic analysis, in order to explain the background and the efficiency as well as the range of application of a number of numerical algorithms which are based on the Fourier-, the wavelet and the Short-Time Fourier Transform (Time-Frequency and Gabor Analysis), as well as other atomic decomposition techniques, in particular in higher dimensions (shearlets, curvelets,...).

There is a wide range of topics to be covered, from the theoretical background (from infinite-dimensional settings, expressed in terms of function spaces to finite dimensional situations) to the development of efficient algorithms and the real-world applications to music- and sound processing or for image analysis tasks.  mathematically oriented lectures will be complemented with practical computer sessions.

The school will be limited to 40 participants. Registration is free but mandatory by June, 30th 2014. Participants can present their work during poster sessions if they want. Abstracts can be submitted by September, 1st 2014.

More information can be found on the dedicated website:
http://feichtingertorresani.weebly.com/information2.html

Three-band linear gutter-bank in Florence (ICASSP 2014)

ICASSP 2014 in Florence has just ended. The slogan was "The art of signal processing". In Florence, Art is indeed everywhere, and science, signal processing included, is not very far apart.

Take for instance this example of an analysis/synthetis three-band, apparently linear, and complex gutter-bank. I do suspect a certain redundancy i cannot yet understand. Is it related to other diffusion-based filter-banks?

Signal processing for chemical sensing (OGST Special issue)

OGST (Oil & Gas Science and Technology) has just published a special issue on "Advances in signal processing and image analysis for physicochemical, analytical chemistry and chemical sensing", vol. 69, number 2 (March-April 2014). It somehow parallels the ICASSP 2013 Special session on  Signal Processing for Chemical Sensing. Moreover, a contributed book in planned on the topic.

The editorial (F. Rocca and L. Duval) deals with informational content of data, sensory principles and, of source, the law of parcimony (beautifully illustrated in "The name of the rose"), Ockham's razor, in other words, sparsity, a common aspect in recent signal processing techniques. So why is the topic interesting for chemical engineers and scientists?

With the advent of more affordable, higher resolution or innovative data acquisition techniques (for instance hyphenated instrumentation such as two-dimensional chromatography), the need for advanced signal and image processing tools has grown in physico-chemical analysis, together with the quantity and complexity of acquired measurements.

Either with mono- (signals) or two-dimensional (from hyphenated techniques to standard images) data, processing generally aims at improving quality and at providing more precise quantitative assessment of measurements of materials and products, to yield insight or access to information, chemical properties, reactive dynamics or textural properties, to name a few (for instance). Although chemometrics embrace from experimental design to calibration, more interplay between physico-chemical analysis and generic signal and image processing is believed to strengthen the two disciplines. Indeed, although they strongly differ in background and vocabulary, both specialities share similar values of best practice in carrying out identifications and comprehensive characterizations, albethey of samples or of numerical data. 

The present call for papers aims at gathering contributions on recent progresses performed and emerging trends concerning (but not limited to):

• 1D and 2D acquisition, sparse sampling (compressive sensing), modulation/demodulation, compression, background/baseline/trend estimation, enhancement, integration, smoothing and filtering, denoising, differentiation, detection, deconvolution and source separation, resolution improvement, peak or curve fitting and matching, clustering, segmentation, multiresolution analysis, mathematical morphology, calibration, multivariate curve resolution, property prediction, regression, data mining, tomography, visualization,

pertaining to the improvement of physico-chemical analysis techniques, including (not exclusively):

• (high-performance) gas, liquid or ion chromatography; gel electrophoresis; diode array detector; Ultraviolet (UV), visible, Infrared (NIR, FIR), Raman or Nuclear Magnetic Resonance (NMR) spectroscopy, X-ray diffraction (XRD), X-Ray Absorption (EXAFS, XANES), mass spectrometry; photoacoustic spectroscopy (PAS); porosimetry; hyphenated techniques; ion-sensitive sensors, artificial noses; electron microscopy (SEM, TEM),

in the following proposed domains:

• catalysis, chemical engineering, oil and gas production, refining processes, petrochemicals, and other sources of energy, in particular alternative energies with a view to sustainable development. 

The published papers follow:

NMR data analysis: A time-domain parametric approach using adaptive subband decomposition [pdf], E.-H. Djermoune, M. Tomczak and D. Brie


Abstract:
This paper presents a fast time-domain data analysis method for one- and two-dimensional Nuclear Magnetic Resonance (NMR) spectroscopy, assuming Lorentzian lineshapes, based on an adaptive spectral decomposition. The latter is achieved through successive filtering and decimation steps ending up in a decomposition tree. At each node of the tree, the parameters of the corresponding subband signal are estimated using some high-resolution method. The resulting estimation error is then processed through a stopping criterion which allows one to decide whether the decimation should be carried on or not. Thus the method leads to an automated selection of the decimation level and consequently to a signal-adaptive decomposition. Moreover, it enables one to reduce the processing time and makes the choice of usual free parameters easier, comparatively to the case where the whole signal is processed at once. The efficiency of the method is demonstrated using 1-D and 2-D 13C NMR signals.

Inverse Problem Approach for Alignment of Electron Tomographic series [pdf], V.-D. Tran, M. Moreaud, É. Thiébaut, L. Denis and J.-M. Becker

Abstract:
In the refining industry, morphological measurements of particles have become an essential part in the characterization catalyst supports. Through these parameters, one can infer the specific physicochemical properties of the studied materials. One of the main acquisition techniques is electron tomography (or nanotomography). 3D volumes are reconstructed from sets of projections from different angles made by a Transmission Electron Microscope (TEM). This technique provides a real three-dimensional information at the nanometric scale. A major issue in this method is the misalignment of the projections that contributes to the reconstruction. The current alignment techniques usually employ fiducial markers such as gold particles for a correct alignment of the images. When the use of markers is not possible, the correlation between adjacent projections is used to align them. However, this method sometimes fails. In this paper, we propose a new method based on the inverse problem approach where a certain criterion is minimized using a variant of the Nelder and Mead simplex algorithm. The proposed approach is composed of two steps. The first step consists of an initial alignment process, which relies on the minimization of a cost function based on robust statistics measuring the similarity of a projection to its previous projections in the series. It reduces strong shifts resulting from the acquisition between successive projections. In the second step, the pre-registered projections are used to initialize an iterative alignment-refinement process which alternates between (i) volume reconstructions and (ii) registrations of measured projections onto simulated projections computed from the volume reconstructed in (i). At the end of this process, we have a correct reconstruction of the volume, the projections being correctly aligned. Our method is tested on simulated data and shown to estimate accurately the translation, rotation and scale of arbitrary transforms. We have successfully tested our method with real projections of different catalyst supports.

Morphological Component Analysis for the Inpainting of Grazing Incidence X-Ray Diffraction Images Used for the Structural Characterization of Thin Films [pdf], George Tzagkarakis, E. Pavlopoulou, J. Fadili, G. Hadziioannou and J.-L. Starck

Abstract:
Grazing Incidence X-ray Diffraction (GIXD) is a widely used characterization technique, applied for the investigation of the structure of thin films. As far as organic films are concerned, the confinement of the film to the substrate results in anisotropic 2-dimensional GIXD patterns, such those observed for polythiophene-based films, which are used as active layers in photovoltaic applications. Potential malfunctions of the detectors utilized may distort the quality of the acquired images, affecting thus the analysis process and the structural information derived. Motivated by the success of Morphological Component Analysis (MCA) in image processing, we tackle in this study the problem of recovering the missing information in GIXD images due to potential detector's malfunction. First, we show that the geometrical structures which are present in the GIXD images can be represented sparsely by means of a combination of over-complete transforms, namely, the curvelet and the undecimated wavelet transform, resulting in a simple and compact description of their inherent information content. Then, the missing information is recovered by applying MCA in an inpainting framework, by exploiting the sparse representation of GIXD data in these two over-complete transform domains. The experimental evaluation shows that the proposed approach is highly efficient in recovering the missing information in the form of either randomly burned pixels, or whole burned rows, even at the order of 50 % of the total number of pixels. Thus, our approach can be applied for healing any potential problems related to detector performance during acquisition, which is of high importance in synchrotron-based experiments, since the beamtime allocated to users is extremely limited and any technical malfunction could be detrimental for the course of the experimental project. Moreover, the non-necessity of long acquisition times or repeating measurements, which stems from our results adds extra value to the proposed approach.

Multivariate Data Analysis for the Processing of Signals [pdf] J. Renwick Beattie

Abstract:
Real-world experiments are becoming increasingly more complex, needing techniques capable of tracking this complexity. Signal based measurements are often used to capture this complexity, where a signal is a record of a sample’s response to a parameter (e.g. time, displacement, voltage, wavelength) that is varied over a range of values. In signals the responses at each value of the varied parameter are related to each other, depending on the composition or state sample being measured. Since signals contain multiple information points, they have rich information content but are generally complex to comprehend. Multivariate Analysis (MA) has profoundly transformed their analysis by allowing gross simplification of the tangled web of variation. In addition MA has also provided the advantage of being much more robust to the influence of noise than univariate methods of analysis. In recent years, there has been a growing awareness that the nature of the multivariate methods allows exploitation of its benefits for purposes other than data analysis, such as pre-processing of signals with the aim of eliminating irrelevant variations prior to analysis of the signal of interest. It has been shown that exploiting multivariate data reduction in an appropriate way can allow high fidelity denoising (removal of irreproducible non-signals), consistent and reproducible noise-insensitive correction of baseline distortions (removal of reproducible non-signals), accurate elimination of interfering signals (removal of reproducible but unwanted signals) and the standardisation of signal amplitude fluctuations. At present, the field is relatively small but the possibilities for much wider application are considerable. Where signal properties are suitable for MA (such as the signal being stationary along the x-axis), these signal based corrections have the potential to be highly reproducible, and highly adaptable and are applicable in situations where the data is noisy or where the variations in the signals can be complex. As science seeks to probe datasets in less and less tightly controlled situations the ability to provide high-fidelity corrections in a very flexible manner is becoming more critical and multivariate based signal processing has the potential to provide many solutions.

Design of Smart Ion-selective Electrode Arrays based on Source Separation through Nonlinear Independent Component Analysis [pdf] Leonardo T. Duarte and Christian Jutten

Abstract:
The development of chemical sensor arrays based on Blind Source Separation (BSS) provides a promising solution to overcome the interference problem associated with Ion-Selective Electrodes (ISE). The main motivation behind this new approach is to ease the time-demanding calibration stage. While the first works on this problem only considered the case in which the ions under analysis have equal valences, the present work aims at developing a BSS technique that works when the ions have different charges. In this situation, the resulting mixing model belongs to a particular class of nonlinear systems that have never been studied in the BSS literature. In order to tackle this sort of mixing process, we adopted a recurrent network as separating system. Moreover, concerning the BSS learning strategy, we develop a mutual information minimization approach based on the notion of the differential of the mutual information. The method works requires a batch operation, and, thus, can be used to perform off-line analysis. The validity of our approach is supported by experiments where the mixing model parameters were extracted from actual data.

Unsupervised segmentation of hyperspectral images with spatialized Gaussian mixture model and model selection [pdf] Serge Cohen, Erwan Le Pennec

Abstract:
In this article, we describe a novel unsupervised spectral image segmentation algorithm. This algorithm extends the classical Gaussian Mixture Model-based unsupervised classification technique by incorporating a spatial flavor into the model: the spectra are modelized by a mixture of K classes, each with a Gaussian distribution, whose mixing proportions depend on the position. Using a piecewise constant structure for those mixing proportions, we are able to construct a penalized maximum likelihood procedure that estimates the optimal partition as well as all the other parameters, including the number of classes. We provide a theoretical guarantee for this estimation, even when the generating model is not within the tested set, and describe an efficient implementation. Finally, we conduct some numerical experiments of unsupervised segmentation from a real dataset.

ICASSP 2014: Tutorials "sive" Florence monuments

Starting tomorrow, the International Conference on Acoustics, Speech and Signal Processing hosts 15 tutorials on solid topics, ranging from convex optimization to big data and signal processing on graphs. 

If you are wealthy enough to have registred, you may download the tutorial support pdf files from the given links, and uncompress them with the password provided with your registration. If not, sive, well, we are in the magnificient Florence, at least 12 key places are worth paying a visit, namely:

palazzovecchio, fortezzadabasso, pontevecchio, santamariadelfiore, palazzopitti, santamarianovella, giardinodiboboli, santacroce, piazzalemichelangelo, campaniledigiotto, sanlorenzo, corridoiovasariano.

If you know three other hidden places, fell free to tell.

T1 - Statistical Signal Processing for Graphs
Subject Area: Fundamentals
Speakers: Nadya T. Bliss (Arizona State University), Alfred O. Hero (University of Michigan, Ann Arbor), Benjamin A. Miller (MIT Lincoln Laboratory)

T2 - Monotone Operator Splitting Methods in Signal and Image Recovery
Subject Area: Image Processing
Speakers: P.L. Combettes (Université Pierre et Marie Curie – Paris 6), J.-C. Pesquet (Université Paris-Est), and N. Pustelnik (ENS de Lyon)

T3 - Informed Audio Source Separation: Trends, Approaches and Algorithms
Subject Area: Speech/Audio/Language Processing
Speaker: Alexey Ozerov (Technicolor), Antoine Liutkus (INRIA, Nancy Grand Est) and Gaël Richard (Telecom ParisTech)

T4 - Signal Processing for Analog Systems
Subject Area: Signal Processing System Design and Implementation
Speakers: Arthur J. Redfern, Manar El-Chammas and Lei Ding (Texas Instruments)

T5 - Transmitter Cooperation in Wireless Networks: Potential and Challenges*
Subject Area: Communications
Speakers: David Gesbert and Paul de Kerret (EURECOM)

T6 - Signal Processing for Big Data
Subject Area: Fundamentals
Speakers: G.B. Giannakis, Konstantinos Slavakis (University of Minnesota), Gonzalo Mateos (Carnegie Mellon University)

T7 - Semidefinite Relaxation: From Theory to Applications to Latest Advances*
Subject Area: Fundamentals
Speakers: Wing-Kin Ma and Anthony Man-Cho So (The Chinese University of Hong Kong)

T8 - EEG Signal Processing and Classification for Brain Computer Interfacing (BCI) Applications
Subject Area: Biomedical signal processing
Speakers: Amit Konar (Jadavpur University), Fabien Lotte (INRIA-Bordeaux Sud-Ouest), Arijit Sinharay (Tata Consultancy Services Ltd)

T9 - Deep learning for natural language processing and related applications
Subject Area: Speech/Audio/Language Processing
Speakers: Xiaodong He, Jianfeng Gao, Li Deng (Microsoft Research)

T10 - Bits and Flops in modern communications: analyzing complexity as the missing piece of the wireless-communication puzzle
Subject Area: Communications
Speakers: Petros Elia (EURECOM) and Joakim Jaldén (Royal Institute of Technology, KTH, Sweden)

T11 - An introduction to sparse stochastic processes
Subject Area: Fundamentals
Speaker: Micheal Unser (EPFL)

T12 - Factoring Tensors in the Cloud: A Tutorial on Big Tensor Data Analytics
Subject Area: Fundamentals
Speakers: Nicholas Sidiropoulos (University of Minnesota) and Evangelos Papalexakis (Carnegie Mellon University)

T13 - Complex elliptically symmetric distributions and their applications in signal processing
Subject Area: Statistical Signal Processing
Speakers: Esa Ollila (Aalto University, Finland), David E. Tyler (Rutgers University) and Frederic Pascal (SUPELEC)

T14 - Signal Processing for Finance, Economics and Marketing Modeling and Information Processing*
Subject Area: Financial data analysis
Speakers: Xiao-Ping (Steven) Zhang (Ryerson University), Fang Wang (Wilfrid Laurier University)

T15 - Signal Processing in Power Line Communication Systems
Subject Area: Communications
Speaker: Andrea M. Tonello (University of Udine, Italy)