146 research outputs found
Proceedings of the 2011 New York Workshop on Computer, Earth and Space Science
The purpose of the New York Workshop on Computer, Earth and Space Sciences is
to bring together the New York area's finest Astronomers, Statisticians,
Computer Scientists, Space and Earth Scientists to explore potential synergies
between their respective fields. The 2011 edition (CESS2011) was a great
success, and we would like to thank all of the presenters and participants for
attending. This year was also special as it included authors from the upcoming
book titled "Advances in Machine Learning and Data Mining for Astronomy". Over
two days, the latest advanced techniques used to analyze the vast amounts of
information now available for the understanding of our universe and our planet
were presented. These proceedings attempt to provide a small window into what
the current state of research is in this vast interdisciplinary field and we'd
like to thank the speakers who spent the time to contribute to this volume.Comment: Author lists modified. 82 pages. Workshop Proceedings from CESS 2011
in New York City, Goddard Institute for Space Studie
Spatial Acoustic Vector Based Sound Field Reproduction
Spatial sound field reproduction aims to recreate an immersive sound field over a spatial region. The existing sound pressure based approaches to spatial sound field reproduction focus on the accurate approximation of original sound pressure over space, which ignores the perceptual accuracy of the reproduced sound field. The acoustic vectors of particle velocity and sound intensity appear to be closely linked with human perception of sound localization in literature. Therefore, in this thesis, we explore the spatial distributions of the acoustic vectors, and seek to develop algorithms to perceptually reproduce the original sound field over a continuous spatial region based on the vectors. A theory of spatial acoustic vectors is first developed, where the spatial distributions of particle velocity and sound intensity are derived from sound pressure. To extract the desired sound pressure from a mixed sound field environment, a 3D sound field separation technique is also formulated. Based on this theory, a series of reproduction techniques are proposed to improve the perceptual performance. The outcomes resulting from this theory are: (i) derivation of a particle velocity assisted 3D sound field reproduction technique which allows for non-uniform loudspeaker geometry with a limited number of loudspeakers, (ii) design of particle velocity based mixed-source sound field translation technique for binaural reproduction that can provide sound field translation with good perceptual experience over a large space, (iii) derivation of an intensity matching technique that can reproduce the desired sound field in a spherical region by controlling the sound intensity on the surface of the region, and (iv) two intensity based multizone sound field reproduction algorithms that can reproduce the desired sound field over multiple spatial zones. Finally, these techniques are evaluated by comparing to the conventional approaches through numerical simulations and real-world experiments
Sound Source Localization and Modeling: Spherical Harmonics Domain Approaches
Sound source localization has been an important research topic in the acoustic signal processing community because of its wide use in many acoustic applications, including speech separation, speech enhancement, sound event detection, automatic speech recognition, automated camera steering, and virtual reality. In the recent decade, there is a growing interest in the research of sound source localization using higher-order microphone arrays, which are capable of recording and analyzing the soundfield over a target spatial area. This thesis studies a novel source feature called the relative harmonic coefficient, that easily estimated from the higher-order microphone measurements. This source feature has direct applications for sound source localization due to its sole dependence on the source position.
This thesis proposes two novel sound source localization algorithms using the relative harmonic coefficients: (i) a low-complexity single source localization approach that localizes the source' elevation and azimuth separately. This approach is also appliable to acoustic enhancement for the higher-order microphone array recordings; (ii) a semi-supervised multi-source localization algorithm in a noisy and reverberant environment. Although this approach uses a learning schema, it still has a strong potential to be implemented in practice because only a limited number of labeled measurements are required. However, this algorithm has an inherent limitation as it requires the availability of single-source components. Thus, it is unusable in scenarios where the original recordings have limited single-source components (e.g., multiple sources simultaneously active). To address this issue, we develop a novel MUSIC framework based approach that directly uses simultaneous multi-source recordings. This developed MUSIC approach uses robust measurements of relative sound pressure from the higher-order microphone and is shown to be more suitable in noisy environments than the traditional MUSIC method.
While the proposed approaches address the source localization problems, in practice, the broader problem of source localization has some more common challenges, which have received less attention. One such challenge is the common assumption of the sound sources being omnidirectional, which is hardly the case with a typical commercial loudspeaker. Therefore, in this thesis, we analyze the broader problem of analyzing directional characteristics of the commercial loudspeakers by deriving equivalent theoretical acoustic models. Several acoustic models are investigated, including plane waves decomposition, point source decomposition, and mixed source decomposition. We finally conduct extensive experimental examinations to see which acoustic model has more similar characteristics with commercial loudspeakers
On-Bird Sound Recordings: Automatic Acoustic Recognition of Activities and Contexts
We introduce a novel approach to studying animal behaviour and the context in
which it occurs, through the use of microphone backpacks carried on the backs
of individual free-flying birds. These sensors are increasingly used by animal
behaviour researchers to study individual vocalisations of freely behaving
animals, even in the field. However such devices may record more than an
animals vocal behaviour, and have the potential to be used for investigating
specific activities (movement) and context (background) within which
vocalisations occur. To facilitate this approach, we investigate the automatic
annotation of such recordings through two different sound scene analysis
paradigms: a scene-classification method using feature learning, and an
event-detection method using probabilistic latent component analysis (PLCA). We
analyse recordings made with Eurasian jackdaws (Corvus monedula) in both
captive and field settings. Results are comparable with the state of the art in
sound scene analysis; we find that the current recognition quality level
enables scalable automatic annotation of audio logger data, given partial
annotation, but also find that individual differences between animals and/or
their backpacks limit the generalisation from one individual to another. we
consider the interrelation of 'scenes' and 'events' in this particular task,
and issues of temporal resolution
An F-ratio-Based Method for Estimating the Number of Active Sources in MEG
Magnetoencephalography (MEG) is a powerful technique for studying the human
brain function. However, accurately estimating the number of sources that
contribute to the MEG recordings remains a challenging problem due to the low
signal-to-noise ratio (SNR), the presence of correlated sources, inaccuracies
in head modeling, and variations in individual anatomy. To address these
issues, our study introduces a robust method for accurately estimating the
number of active sources in the brain based on the F-ratio statistical
approach, which allows for a comparison between a full model with a higher
number of sources and a reduced model with fewer sources. Using this approach,
we developed a formal statistical procedure that sequentially increases the
number of sources in the multiple dipole localization problem until all sources
are found. Our results revealed that the selection of thresholds plays a
critical role in determining the method`s overall performance, and appropriate
thresholds needed to be adjusted for the number of sources and SNR levels,
while they remained largely invariant to different inter-source correlations,
modeling inaccuracies, and different cortical anatomies. By identifying optimal
thresholds and validating our F-ratio-based method in simulated, real phantom,
and human MEG data, we demonstrated the superiority of our F-ratio-based method
over existing state-of-the-art statistical approaches, such as the Akaike
Information Criterion (AIC) and Minimum Description Length (MDL). Overall, when
tuned for optimal selection of thresholds, our method offers researchers a
precise tool to estimate the true number of active brain sources and accurately
model brain function
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Numerical techniques for near-field acoustic holography
Near-field acoustic holography (NAH) has proved to be an enormously successful sound source identification technique, which is widely used in field of acoustics and noise control. The work presented in this thesis aims at developing a novel method for modelling near-field acoustic holography, where the particle velocity on the surface of a vibrating structure is recovered from measurements taken in the exterior field. The model developed will form a powerful predictive tool for identifying sources of acoustic radiation for applications in mechanical and audio engineering.
Inspired by advances in the solution of ill-posed problems in imaging science using the so-called compressed sensing, we seek to develop new compressed or sparse reconstruction methods for the NAH problem. A sparse superposition method will be developed and implemented based on the inverse method of superposition (MoS), or equivalent source method as it is often known. The method should be able to reconstruct the normal velocity of a vibrating object using a very small number of charge points. Two primary reasons this is beneficial are; one can potentially reduce the amount of measured data required, and one could also detect sources of noise when small clusters of charge points are identified. The sparse inverse MoS will then be applied to reconstruct the surface velocity of a loudspeaker from measurements of the sound pressure field taken in a semi-anechoic chamber. The development of the new sparse inverse MoS and its experimental verification form the primary contributions to knowledge of this thesis
Reconstruction of enhanced ultrasound images from compressed measurements
L'intérêt de l'échantillonnage compressé dans l'imagerie ultrasonore a été récemment évalué largement par plusieurs équipes de recherche. Suite aux différentes configurations d'application, il a été démontré que les données RF peuvent être reconstituées à partir d'un faible nombre de mesures et / ou en utilisant un nombre réduit d'émission d'impulsions ultrasonores. Selon le modèle de l'échantillonnage compressé, la résolution des images ultrasonores reconstruites à partir des mesures compressées dépend principalement de trois aspects: la configuration d'acquisition, c.à .d. l'incohérence de la matrice d'échantillonnage, la régularisation de l'image, c.à .d. l'a priori de parcimonie et la technique d'optimisation. Nous nous sommes concentrés principalement sur les deux derniers aspects dans cette thèse. Néanmoins, la résolution spatiale d'image RF, le contraste et le rapport signal sur bruit dépendent de la bande passante limitée du transducteur d'imagerie et du phénomène physique lié à la propagation des ondes ultrasonores. Pour surmonter ces limitations, plusieurs techniques de traitement d'image en fonction de déconvolution ont été proposées pour améliorer les images ultrasonores. Dans cette thèse, nous proposons d'abord un nouveau cadre de travail pour l'imagerie ultrasonore, nommé déconvolution compressée, pour combiner l'échantillonnage compressé et la déconvolution. Exploitant une formulation unifiée du modèle d'acquisition directe, combinant des projections aléatoires et une convolution 2D avec une réponse impulsionnelle spatialement invariante, l'avantage de ce cadre de travail est la réduction du volume de données et l'amélioration de la qualité de l'image. Une méthode d'optimisation basée sur l'algorithme des directions alternées est ensuite proposée pour inverser le modèle linéaire, en incluant deux termes de régularisation exprimant la parcimonie des images RF dans une base donnée et l'hypothèse statistique gaussienne généralisée sur les fonctions de réflectivité des tissus. Nous améliorons les résultats ensuite par la méthode basée sur l'algorithme des directions simultanées. Les deux algorithmes sont évalués sur des données simulées et des données in vivo. Avec les techniques de régularisation, une nouvelle approche basée sur la minimisation alternée est finalement développée pour estimer conjointement les fonctions de réflectivité des tissus et la réponse impulsionnelle. Une investigation préliminaire est effectuée sur des données simulées.The interest of compressive sampling in ultrasound imaging has been recently extensively evaluated by several research teams. Following the different application setups, it has been shown that the RF data may be reconstructed from a small number of measurements and/or using a reduced number of ultrasound pulse emissions. According to the model of compressive sampling, the resolution of reconstructed ultrasound images from compressed measurements mainly depends on three aspects: the acquisition setup, i.e. the incoherence of the sampling matrix, the image regularization, i.e. the sparsity prior, and the optimization technique. We mainly focused on the last two aspects in this thesis. Nevertheless, RF image spatial resolution, contrast and signal to noise ratio are affected by the limited bandwidth of the imaging transducer and the physical phenomenon related to Ultrasound wave propagation. To overcome these limitations, several deconvolution-based image processing techniques have been proposed to enhance the ultrasound images. In this thesis, we first propose a novel framework for Ultrasound imaging, named compressive deconvolution, to combine the compressive sampling and deconvolution. Exploiting an unified formulation of the direct acquisition model, combining random projections and 2D convolution with a spatially invariant point spread function, the benefit of this framework is the joint data volume reduction and image quality improvement. An optimization method based on the Alternating Direction Method of Multipliers is then proposed to invert the linear model, including two regularization terms expressing the sparsity of the RF images in a given basis and the generalized Gaussian statistical assumption on tissue reflectivity functions. It is improved afterwards by the method based on the Simultaneous Direction Method of Multipliers. Both algorithms are evaluated on simulated and in vivo data. With regularization techniques, a novel approach based on Alternating Minimization is finally developed to jointly estimate the tissue reflectivity function and the point spread function. A preliminary investigation is made on simulated data
Environmental model-based time-reversal underwater communications
Advances in underwater acoustic communications require the development of methods to
accurately compensate channels that are prone to severe double spreading of time-varying
multipath propagation, fading and signal phase variations. Assuming the environmental
information as a key issue, this work aims to improve communications performance
of single-input-multiple-output transmission systems in such channels through the enhancement
of their estimates used for equalization. The acoustic propagation physical
parameters of the environment between the source and the receivers are considered in
the process. The approach is to mitigate noise e ects in channel identi cation for Passive
Time-Reversal (PTR), which is a low complexity probe-based refocusing technique to
reduce time spreading and inter-symbol interference. The method Environmental-based
PTR (EPTR) is proposed that, inspired by matched eld inversion, inserts physics of
acoustic propagation in the channel compensation procedure through ray trace modeling
and environmental focalization processing. The focalization is the process of tweaking
the environmental parameters to obtain a noise-free numerical model generated channel
response that best matches the observed data. The EPTR performance is tested and
compared to the pulse-compressed PTR and to the regularized `1-norm PTR. The former
is based on classical `2-norm channel estimation and the latter, inspired by compressive
sensing, uses weighted `1-norm into the `2-norm estimation problem to obtain improved
estimates of sparse channels. Successful experimental results were obtained with the proposed
method for signals containing image messages transmitted at 4 kbit/s from a source
to a 16-hydrophones vertical array at 890 m range during the UAN'11 experiment conducted
o the coast of Trondheim (Norway). The scienti c contributions of this work are
(i) the understanding of the process of employing physical modeling and environmental
focalization to equalize and retrieve received messages in underwater acoustic communications,
thus exploiting the sensitivity of environmental parameters in order to adapt a
communications system to the scenario where it is used; and (ii) the presentation of a new
PTR-based method that focuses environmental parameters to model suitable noise-free
channel responses for equalization and whose real data results were successful for a set
of coherent signals collected at sea. The proposed method is a step forward to a better
understanding on how to insert physical knowledge of the environment for equalization in
digital underwater acoustic communications
Computational Inverse Problems for Partial Differential Equations
The problem of determining unknown quantities in a PDE from measurements of (part of) the solution to this PDE arises in a wide range of applications in science, technology, medicine, and finance. The unknown quantity may e.g. be a coefficient, an initial or a boundary condition, a source term, or the shape of a boundary. The identification of such quantities is often computationally challenging and requires profound knowledge of the analytical properties of the underlying PDE as well as numerical techniques. The focus of this workshop was on applications in phase retrieval, imaging with waves in random media, and seismology of the Earth and the Sun, a further emphasis was put on stochastic aspects in the context of uncertainty quantification and parameter identification in stochastic differential equations. Many open problems and mathematical challenges in application fields were addressed, and intensive discussions provided an insight into the high potential of joining deep knowledge in numerical analysis, partial differential equations, and regularization, but also in mathematical statistics, homogenization, optimization, differential geometry, numerical linear algebra, and variational analysis to tackle these challenges
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