Non-negative matrix decomposition approaches to frequency domain analysis of music audio signals

On étudie l’application des algorithmes de décomposition matricielles tel que la Factorisation Matricielle Non-négative (FMN), aux représentations fréquentielles de signaux audio musicaux. Ces algorithmes, dirigés par une fonction d’erreur de reconstruction, apprennent un ensemble de fonctions de base et un ensemble de coef- ficients correspondants qui approximent le signal d’entrée. On compare l’utilisation de trois fonctions d’erreur de reconstruction quand la FMN est appliquée à des gammes monophoniques et harmonisées: moindre carré, divergence Kullback-Leibler, et une mesure de divergence dépendente de la phase, introduite récemment. Des nouvelles méthodes pour interpréter les décompositions résultantes sont présentées et sont comparées aux méthodes utilisées précédemment qui nécessitent des connaissances du domaine acoustique. Finalement, on analyse la capacité de généralisation des fonctions de bases apprises par rapport à trois paramètres musicaux: l’amplitude, la durée et le type d’instrument. Pour ce faire, on introduit deux algorithmes d’étiquetage des fonctions de bases qui performent mieux que l’approche précédente dans la majorité de nos tests, la tâche d’instrument avec audio monophonique étant la seule exception importante.

Photosensitivity of Laser Dye Mixtures in Polymer Matrix – A Photoacoustic Study

Laser induced photoacoustic (PA) technique is used in the study of photostability of polymethyl methacrylate (PMMA) films doped with Rhodamine 6G -Rhodamine B dye system. Energy transfer from a donor molecule to an acceptor molecule in a dye mixture affects the output of the dye system. Details of investigations on the role of laser power, modulation frequency and the irradiation wavelength on the photosensitivity of the dye mixture doped PMMA films are presented.

Laser-induced thermal characterization of nano Ag metal dispersed ceramic alumina matrix

In this paper, we report the measurements of thermal diffusivity of nano Ag metal dispersed ceramic alumina matrix sintered at different temperatures using laser induced non-destructive photoacoustic technique. Measurements of thermal diffusivity also have been carried out on specimens with various concentration of nano metal. Analysis of the data is done on the basis of one-dimensional model of Rosencwaig and Gersho. The present measurements on the thermal diffusivity of nano metal dispersed ceramic alumina shows that porosity has a great influence on the heat transport and the thermal diffusivity value. The present analysis also shows that the inclusion of nano metal into ceramic matrix increases its interconnectivity and hence the thermal diffusivity value. The present study on the samples sintered at different temperature shows that the porosity of the ceramics varies considerably with the change in sintering temperature. The results are interpreted in terms of phonon assisted heat transfer mechanism and the exclusion of pores with the increase in sintering temperature.

Laser-induced thermal characterization of nano Ag metal dispersed ceramic alumina matrix

In this paper, we report the measurements of thermal diffusivity of nano Ag metal dispersed ceramic alumina matrix sintered at different temperatures using laser induced non-destructive photoacoustic technique. Measurements of thermal diffusivity also have been carried out on specimens with various concentration of nano metal. Analysis of the data is done on the basis of one-dimensional model of Rosencwaig and Gersho. The present measurements on the thermal diffusivity of nano metal dispersed ceramic alumina shows that porosity has a great influence on the heat transport and the thermal diffusivity value. The present analysis also shows that the inclusion of nano metal into ceramic matrix increases its interconnectivity and hence the thermal diffusivity value. The present study on the samples sintered at different temperature shows that the porosity of the ceramics varies considerably with the change in sintering temperature. The results are interpreted in terms of phonon assisted heat transfer mechanism and the exclusion of pores with the increase in sintering temperature

Laser-induced thermal characterization of nano Ag metal dispersed ceramic alumina matrix

In this paper, we report the measurements of thermal diffusivity of nano Ag metal dispersed ceramic alumina matrix sintered at different temperatures using laser induced non-destructive photoacoustic technique. Measurements of thermal diffusivity also have been carried out on specimens with various concentration of nano metal. Analysis of the data is done on the basis of one-dimensional model of Rosencwaig and Gersho. The present measurements on the thermal diffusivity of nano metal dispersed ceramic alumina shows that porosity has a great influence on the heat transport and the thermal diffusivity value. The present analysis also shows that the inclusion of nano metal into ceramic matrix increases its interconnectivity and hence the thermal diffusivity value. The present study on the samples sintered at different temperature shows that the porosity of the ceramics varies considerably with the change in sintering temperature. The results are interpreted in terms of phonon assisted heat transfer mechanism and the exclusion of pores with the increase in sintering temperature

Characterization of Selected Photonic Materials and Systems Using Photoacoustic Technique

The photoacoustic investigations carried out on different photonic materials are presented in this thesis. Photonic materials selected for the investigation are tape cast ceramics, muItilayer dielectric coatings, organic dye doped PVA films and PMMA matrix doped with dye mixtures. The studies are performed by the measurement of photoacoustic signal generated as a result of modulated cw laser irradiation of samples. The gas-microphone scheme is employed for the detection of photoacoustic signal. The different measurements reported here reveal the adaptability and utility of the PA technique for the characterization of photonic materials.Ceramics find applications in the field of microelectronics industry. Tape cast ceramics are the building blocks of many electronic components and certain ceramic tapes are used as thermal barriers. The thermal parameters of these tapes will not be the same as that of thin films of the same materials. Parameters are influenced by the presence of foreign bodies in the matrix and the sample preparation technique. Measurements are done on ceramic tapes of Zirconia, Zirconia-Alumina combination, barium titanate, barium tin titanate, silicon carbide, lead zirconate titanateil'Z'T) and lead magnesium niobate titanate(PMNPT). Various configurations viz. heat reflection geometry and heat transmission geometry of the photoacoustic technique have been used for the evaluation of different thermal parameters of the sample. Heat reflection geometry of the PA cell has been used for the evaluation of thermal effusivity and heat transmission geometry has been made use of in the evaluation of thermal diffusivity. From the thermal diffusivity and thermal effusivity values, thermal conductivity is also calculated. The calculated values are nearly the same as the values reported for pure materials. This shows the feasibility of photoacoustic technique for the thermal characterization of ceramic tapes.Organic dyes find applications as holographic recording medium and as active media for laser operations. Knowledge of the photochemical stability of the material is essential if it has to be used tor any of these applications. Mixing one dye with another can change the properties of the resulting system. Through careful mixing of the dyes in appropriate proportions and incorporating them in polymer matrices, media of required stability can be prepared. Investigations are carried out on Rhodamine 6GRhodamine B mixture doped PMMA samples. Addition of RhB in small amounts is found to stabilize Rh6G against photodegradation and addition of Rh6G into RhB increases the photosensitivity of the latter. The PA technique has been successfully employed for the monitoring of dye mixture doped PMMA sample. The same technique has been used for the monitoring of photodegradation ofa laser dye, cresyl violet doped polyvinyl alcohol also.Another important application of photoacoustic technique is in nondestructive evaluation of layered samples. Depth profiling capability of PA technique has been used for the non-destructive testing of multilayer dielectric films, which are highly reflecting in the wavelength range selected for investigations. Eventhough calculation of thickness of the film is not possible, number of layers present in the system can be found out using PA technique. The phase plot has clear step like discontinuities, the number of which coincides with the number of layers present in the multilayer stack. This shows the sensitivity of PA signal phase to boundaries in a layered structure. This aspect of PA signal can be utilized in non-destructive depth profiling of reflecting samples and for the identification of defects in layered structures.

An Equivalence Between Sparse Approximation and Support Vector Machines

In the first part of this paper we show a similarity between the principle of Structural Risk Minimization Principle (SRM) (Vapnik, 1982) and the idea of Sparse Approximation, as defined in (Chen, Donoho and Saunders, 1995) and Olshausen and Field (1996). Then we focus on two specific (approximate) implementations of SRM and Sparse Approximation, which have been used to solve the problem of function approximation. For SRM we consider the Support Vector Machine technique proposed by V. Vapnik and his team at AT&T Bell Labs, and for Sparse Approximation we consider a modification of the Basis Pursuit De-Noising algorithm proposed by Chen, Donoho and Saunders (1995). We show that, under certain conditions, these two techniques are equivalent: they give the same solution and they require the solution of the same quadratic programming problem.

Rank estimation of trajectory matrix in motion segmentation

A novel technique for estimating the rank of the trajectory matrix in the local subspace affinity (LSA) motion segmentation framework is presented. This new rank estimation is based on the relationship between the estimated rank of the trajectory matrix and the affinity matrix built with LSA. The result is an enhanced model selection technique for trajectory matrix rank estimation by which it is possible to automate LSA, without requiring any a priori knowledge, and to improve the final segmentation

Very large inverse problems in atmosphere and ocean modelling

For the very large nonlinear dynamical systems that arise in a wide range of physical, biological and environmental problems, the data needed to initialize a numerical forecasting model are seldom available. To generate accurate estimates of the expected states of the system, both current and future, the technique of ‘data assimilation’ is used to combine the numerical model predictions with observations of the system measured over time. Assimilation of data is an inverse problem that for very large-scale systems is generally ill-posed. In four-dimensional variational assimilation schemes, the dynamical model equations provide constraints that act to spread information into data sparse regions, enabling the state of the system to be reconstructed accurately. The mechanism for this is not well understood. Singular value decomposition techniques are applied here to the observability matrix of the system in order to analyse the critical features in this process. Simplified models are used to demonstrate how information is propagated from observed regions into unobserved areas. The impact of the size of the observational noise and the temporal position of the observations is examined. The best signal-to-noise ratio needed to extract the most information from the observations is estimated using Tikhonov regularization theory. Copyright © 2005 John Wiley & Sons, Ltd.

Influence-matrix diagnostic of a data assimilation system

The influence matrix is used in ordinary least-squares applications for monitoring statistical multiple-regression analyses. Concepts related to the influence matrix provide diagnostics on the influence of individual data on the analysis - the analysis change that would occur by leaving one observation out, and the effective information content (degrees of freedom for signal) in any sub-set of the analysed data. In this paper, the corresponding concepts have been derived in the context of linear statistical data assimilation in numerical weather prediction. An approximate method to compute the diagonal elements of the influence matrix (the self-sensitivities) has been developed for a large-dimension variational data assimilation system (the four-dimensional variational system of the European Centre for Medium-Range Weather Forecasts). Results show that, in the boreal spring 2003 operational system, 15% of the global influence is due to the assimilated observations in any one analysis, and the complementary 85% is the influence of the prior (background) information, a short-range forecast containing information from earlier assimilated observations. About 25% of the observational information is currently provided by surface-based observing systems, and 75% by satellite systems. Low-influence data points usually occur in data-rich areas, while high-influence data points are in data-sparse areas or in dynamically active regions. Background-error correlations also play an important role: high correlation diminishes the observation influence and amplifies the importance of the surrounding real and pseudo observations (prior information in observation space). Incorrect specifications of background and observation-error covariance matrices can be identified, interpreted and better understood by the use of influence-matrix diagnostics for the variety of observation types and observed variables used in the data assimilation system. Copyright © 2004 Royal Meteorological Society

Kernel classifier construction using orthogonal forward selection and boosting with Fisher ratio class separability measure

A greedy technique is proposed to construct parsimonious kernel classifiers using the orthogonal forward selection method and boosting based on Fisher ratio for class separability measure. Unlike most kernel classification methods, which restrict kernel means to the training input data and use a fixed common variance for all the kernel terms, the proposed technique can tune both the mean vector and diagonal covariance matrix of individual kernel by incrementally maximizing Fisher ratio for class separability measure. An efficient weighted optimization method is developed based on boosting to append kernels one by one in an orthogonal forward selection procedure. Experimental results obtained using this construction technique demonstrate that it offers a viable alternative to the existing state-of-the-art kernel modeling methods for constructing sparse Gaussian radial basis function network classifiers. that generalize well.

Sparse model identification using orthogonal forward regression with basis pursuit and D-optimality

An efficient model identification algorithm for a large class of linear-in-the-parameters models is introduced that simultaneously optimises the model approximation ability, sparsity and robustness. The derived model parameters in each forward regression step are initially estimated via the orthogonal least squares (OLS), followed by being tuned with a new gradient-descent learning algorithm based on the basis pursuit that minimises the l(1) norm of the parameter estimate vector. The model subset selection cost function includes a D-optimality design criterion that maximises the determinant of the design matrix of the subset to ensure model robustness and to enable the model selection procedure to automatically terminate at a sparse model. The proposed approach is based on the forward OLS algorithm using the modified Gram-Schmidt procedure. Both the parameter tuning procedure, based on basis pursuit, and the model selection criterion, based on the D-optimality that is effective in ensuring model robustness, are integrated with the forward regression. As a consequence the inherent computational efficiency associated with the conventional forward OLS approach is maintained in the proposed algorithm. Examples demonstrate the effectiveness of the new approach.

A molecular T-matrix approach to calculating low-energy electron diffusion intensities for ordered molecular adsorbates

We present a novel approach to calculating Low-Energy Electron Diffraction (LEED) intensities for ordered molecular adsorbates. First, the intra-molecular multiple scattering is computed to obtain a non-diagonal molecular T-matrix. This is then used to represent the entire molecule as a single scattering object in a conventional LEED calculation, where the Layer Doubling technique is applied to assemble the different layers, including the molecular ones. A detailed comparison with conventional layer-type LEED calculations is provided to ascertain the accuracy of this scheme of calculation. Advantages of this scheme for problems involving ordered arrays of molecules adsorbed on surfaces are discussed.

Regression based D-optimality experimental design for sparse kernel density estimation

This paper derives an efficient algorithm for constructing sparse kernel density (SKD) estimates. The algorithm first selects a very small subset of significant kernels using an orthogonal forward regression (OFR) procedure based on the D-optimality experimental design criterion. The weights of the resulting sparse kernel model are then calculated using a modified multiplicative nonnegative quadratic programming algorithm. Unlike most of the SKD estimators, the proposed D-optimality regression approach is an unsupervised construction algorithm and it does not require an empirical desired response for the kernel selection task. The strength of the D-optimality OFR is owing to the fact that the algorithm automatically selects a small subset of the most significant kernels related to the largest eigenvalues of the kernel design matrix, which counts for the most energy of the kernel training data, and this also guarantees the most accurate kernel weight estimate. The proposed method is also computationally attractive, in comparison with many existing SKD construction algorithms. Extensive numerical investigation demonstrates the ability of this regression-based approach to efficiently construct a very sparse kernel density estimate with excellent test accuracy, and our results show that the proposed method compares favourably with other existing sparse methods, in terms of test accuracy, model sparsity and complexity, for constructing kernel density estimates.

Sparse polynomial approximation in positive order Sobolev spaces with bounded mixed derivatives and applications to elliptic problems with random loading

In the present paper we study the approximation of functions with bounded mixed derivatives by sparse tensor product polynomials in positive order tensor product Sobolev spaces. We introduce a new sparse polynomial approximation operator which exhibits optimal convergence properties in L2 and tensorized View the MathML source simultaneously on a standard k-dimensional cube. In the special case k=2 the suggested approximation operator is also optimal in L2 and tensorized H1 (without essential boundary conditions). This allows to construct an optimal sparse p-version FEM with sparse piecewise continuous polynomial splines, reducing the number of unknowns from O(p2), needed for the full tensor product computation, to View the MathML source, required for the suggested sparse technique, preserving the same optimal convergence rate in terms of p. We apply this result to an elliptic differential equation and an elliptic integral equation with random loading and compute the covariances of the solutions with View the MathML source unknowns. Several numerical examples support the theoretical estimates.