Sparse separation of sources in 3D soundscapes

A novel blind source separation algorithm applicable to extracting sources from within 3D soundscapes is presented. The algorithm is based on constructing a binary mask based on directional information. The validity of filtering using binary masked based on the ω-disjoint assumption is examined for several typical scenarios. Results for these test environments show an improvement by an order of magnitude when compared to similar work using speech mixtures. Also presented is the novel application of a dual-tree complex wavelet transform to sparse source separation, providing an alternative transformation to the short-time Fourier transform often used in this area. Results are presented showing compara- ble signal-to-interference performance, and significantly improved signal-to-distortion performance when compared against the short time Fourier transform. Results presented for the separation algorithm include quantitative measures of the separation performance for robust comparison against other separation algorithms. Consideration is given to the related problem of localising sources within 3D sound- scapes. Two novel methods are presented, the first using a peak estimation on a spherical histogram constructed using a geodesic grid, the second by adapting a self learning plastic self-organising map to operate on the surface of a unit sphere. It is concluded that the separation algorithm presented is effective for soundscapes comprising ecological or zoological sources. Specific areas for further work are recog- nised, both in terms of isolated technologies and towards the integration of this work into an instrument for soundscape recognition, evaluation and identification.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)

The implicit objective of the biennial "international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST) is to foster collaboration between international scientific teams by disseminating ideas through both specific oral/poster presentations and free discussions. For its second edition, the iTWIST workshop took place in the medieval and picturesque town of Namur in Belgium, from Wednesday August 27th till Friday August 29th, 2014. The workshop was conveniently located in "The Arsenal" building within walking distance of both hotels and town center. iTWIST'14 has gathered about 70 international participants and has featured 9 invited talks, 10 oral presentations, and 14 posters on the following themes, all related to the theory, application and generalization of the "sparsity paradigm": Sparsity-driven data sensing and processing; Union of low dimensional subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph sensing/processing; Blind inverse problems and dictionary learning; Sparsity and computational neuroscience; Information theory, geometry and randomness; Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?; Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website: http://sites.google.com/site/itwist1

Development of new signal analysis methods as preoperative predictors of the Cox-Maze procedure outcome in atrial fibrillation

Atrial fibrilation (AF) is the most common cardiac arrhythmia, however, the knowledge about its causes and mechanisms is still uncompleted. Several studies suggest that atrial structural and electrophysiological remodeling are directly related to its development and perpetuation. To this respect, ECG and preoperative clinical data have been studied to analyze different aspects of atrial remodeling. Nonetheless, there is a lack of studies using ECG parameters to provide valuable clinical information in the study of AF aggressive treatments, such as the Cox-Maze surgery. In this work, ECG parameters such as fibrillatory (f) waves organization and amplitude are studied to predict patient's rhythm from the discharge after the Cox-Maze surgery until a twelve months follow up period. On the other hand, widely used clinical parameters such as age, AF duration and left atrial size (LA size) are studied to assess electrocardiographic results. In addition, clinical information known as a risk factor to develop AF such as weight and body mass index has also been analyze. After assess the individual indices, classification models were created in order to optimize the prediction capability. The results obtained reported that the ECG indices outperform the cinical indices. Nevertheless, the information contained in both types of indices is complementary as the generation of a classification model combining the indices shows. This model exceeded 90% accuracy in each period analyzed. In conclusion, studying the AF information contained in an ECG could provide new data to understand the AF and also could help to develop a reliable method to predict preoperatively the Cox-Maze outcome.La fibrilación auricular (FA) es la arritmia cardiaca más comúnmente encontrada en la práctica clínica diaria, sin embargo, todavía no se comprenden completamente los mecanismos fisiológicos que causan el inicio y la perpetuación de la FA. Diversos estudios sugieren que el remodelado estructural y electrofisiológico de la aurícula está relacionado directamente con el desarrollo y perpetuación de la FA. En este sentido, se ha estudiado el ECG e información clínica preoperatoria para analizar distintos aspectos del remodelado. Sin embargo, hay una falta de estudios usando parámetros electrocardiográficos para proporcionar información clínica valiosa en el estudio de tratamientos agresivos de la FA como la cirugía Cox-Maze. En este trabajo, se estudian parámetros electrocardiográficos como la organización de las ondas fibrilatorias y su amplitud para predecir el ritmo de los pacientes desde el momento del alta, tras la cirugía Cox-Maze hasta 12 meses después de la operación. Por otro lado, para evaluar la capacidad de dichos índices, se han utilizado parámetros clínicos ampliamente utilizados como la edad, el tamaño de la aurícula izquierda y el tiempo en FA. Además, se han estudiado también parámetros clínicos conocidos como factores de riesgo para desarrollar FA como son el peso y el índice de masa corporal. Tras analizar la capacidad predictiva de los índices individualmente, éstos se han combinado mediante la generación de modelos de predicción para optimizar la precisión de las predicciones. Los resultados obtenidos señalan que la información contenida en el ECG obtuvo resultados estadísticamente significativos y predicciones más precisas que los índices clínicos. No obstante, el desarrollo de modelos de predicción combinando ambos tipos de índices superó al uso de éstos por separado, con resultados por encima del 90% en todos los períodos estudiados. En conclusión, el análisis del ECG podría aportar nuevos enfoques a la hora de estudiar la FA, y su uso como herramienta de predicción podría ayudar a desarrollar tratamientos más eficientes y personalizados.La fibril·lació auricular (FA) és l'arítmia cardíaca més comunament trobada en la pràctica clínica diària, no obstant això, encara no es comprenen completament els mecanismes fisiològics que causen l'inici i la perpetuació de la FA. Diversos estudis suggerixen que el remodelat estructural i electrofisiològic de l'aurícula està relacionat directament amb el desenrotllament i perpetuació de la FA. En este sentit, s'ha estudiat l'ECG i informació clínica preoperatòria per a analitzar distints aspectes del remodelat. No obstant això, hi ha una falta d'estudis usant paràmetres electrocardiográficos per a proporcionar informació clínica valuosa en l'estudi de tractaments agressius de la FA com la cirurgia Cox-Maze. En este treball, s'estudien paràmetres electrocardiográficos com l'organització de les ones fibrilatorias i la seua amplitud per a predir el ritme dels pacients des del moment de l'alta, després de la cirurgia Cox-Maze fins a 12 mesos després de l'operació. Per un altre costat per a avaluar la capacitat dels dits índexs, s'han utilitzat paràmetres clínics àmpliament utilitzats com l'edat, la grandària de l'aurícula esquerra i el temps en FA. A més, s'han estudiat també paràmetres clínics coneguts com a factors de risc per a desenrotllar FA com són el pes i l'índex de massa corporal. Després d'analitzar la capacitat predictiva dels índexs individualment, estos s'han combinat per mitjà de la generació de models de predicció per a optimitzar la precisió de les prediccions. Els resultats obtinguts assenyalen que la informació continguda en l'ECG va obtindre resultats estadísticament significatius i prediccions més precises que els índexs clínics. No obstant això, el desenrotllament de models de predicció combinant ambdós tipus d'índexs va superar a l'ús d'estos per separat, amb resultats per damunt del 90% en tots els períodes estudiats. En conclusió, l'anàlisi de l'ECG podria aportar nous enfocaments a l'hora d'estudiar la FA, i el seu ús com a ferramenta de predicció podria ajudar a desenrotllar tractaments més eficients i personalitzats.Hernández Alonso, A. (2017). Development of new signal analysis methods as preoperative predictors of the Cox-Maze procedure outcome in atrial fibrillation [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/90491TESI

Recent Advances in Steganography

Steganography is the art and science of communicating which hides the existence of the communication. Steganographic technologies are an important part of the future of Internet security and privacy on open systems such as the Internet. This book's focus is on a relatively new field of study in Steganography and it takes a look at this technology by introducing the readers various concepts of Steganography and Steganalysis. The book has a brief history of steganography and it surveys steganalysis methods considering their modeling techniques. Some new steganography techniques for hiding secret data in images are presented. Furthermore, steganography in speeches is reviewed, and a new approach for hiding data in speeches is introduced

Dictionary Learning for Sparse Representations With Applications to Blind Source Separation.

During the past decade, sparse representation has attracted much attention in the signal processing community. It aims to represent a signal as a linear combination of a small number of elementary signals called atoms. These atoms constitute a dictionary so that a signal can be expressed by the multiplication of the dictionary and a sparse coefficients vector. This leads to two main challenges that are studied in the literature, i.e. sparse coding (find the coding coefficients based on a given dictionary) and dictionary design (find an appropriate dictionary to fit the data). Dictionary design is the focus of this thesis. Traditionally, the signals can be decomposed by the predefined mathematical transform, such as discrete cosine transform (DCT), which forms the so-called analytical approach. In recent years, learning-based methods have been introduced to adapt the dictionary from a set of training data, leading to the technique of dictionary learning. Although this may involve a higher computational complexity, learned dictionaries have the potential to offer improved performance as compared with predefined dictionaries. Dictionary learning algorithm is often achieved by iteratively executing two operations: sparse approximation and dictionary update. We focus on the dictionary update step, where the dictionary is optimized with a given sparsity pattern. A novel framework is proposed to generalize benchmark mechanisms such as the method of optimal directions (MOD) and K-SVD where an arbitrary set of codewords and the corresponding sparse coefficients are simultaneously updated, hence the term simultaneous codeword optimization (SimCO). Moreover, its extended formulation ‘regularized SimCO’ mitigates the major bottleneck of dictionary update caused by the singular points. First and second order optimization procedures are designed to solve the primitive and regularized SimCO. In addition, a tree-structured multi-level representation of dictionary based on clustering is used to speed up the optimization process in the sparse coding stage. This novel dictionary learning algorithm is also applied for solving the underdetermined blind speech separation problem, leading to a multi-stage method, where the separation problem is reformulated as a sparse coding problem, with the dictionary being learned by an adaptive algorithm. Using mutual coherence and sparsity index, the performance of a variety of dictionaries for underdetermined speech separation is compared and analyzed, such as the dictionaries learned from speech mixtures and ground truth speech sources, as well as those predefined by mathematical transforms. Finally, we propose a new method for joint dictionary learning and source separation. Different from the multistage method, the proposed method can simultaneously estimate the mixing matrix, the dictionary and the sources in an alternating and blind manner. The advantages of all the proposed methods are demonstrated over the state-of-the-art methods using extensive numerical tests

Guided Matching Pursuit and its Application to Sound Source Separation

In the last couple of decades there has been an increasing interest in the application of source separation technologies to musical signal processing. Given a signal that consists of a mixture of musical sources, source separation aims at extracting and/or isolating the signals that correspond to the original sources. A system capable of high quality source separation could be an invaluable tool for the sound engineer as well as the end user. Applications of source separation include, but are not limited to, remixing, up-mixing, spatial re-configuration, individual source modification such as filtering, pitch detection/correction and time stretching, music transcription, voice recognition and source-specific audio coding to name a few. Of particular interest is the problem of separating sources from a mixture comprising two channels (2.0 format) since this is still the most commonly used format in the music industry and most domestic listening environments. When the number of sources is greater than the number of mixtures (which is usually the case with stereophonic recordings) then the problem of source separation becomes under-determined and traditional source separation techniques, such as “Independent Component Analysis” (ICA) cannot be successfully applied. In such cases a family of techniques known as “Sparse Component Analysis” (SCA) are better suited. In short a mixture signal is decomposed into a new domain were the individual sources are sparsely represented which implies that their corresponding coefficients will have disjoint (or almost) disjoint supports. Taking advantage of this property along with the spatial information within the mixture and other prior information that could be available, it is possible to identify the sources in the new domain and separate them by going back to the time domain. It is a fact that sparse representations lead to higher quality separation. Regardless, the most commonly used front-end for a SCA system is the ubiquitous short-time Fourier transform (STFT) which although is a sparsifying transform it is not the best choice for this job. A better alternative is the matching pursuit (MP) decomposition. MP is an iterative algorithm that decomposes a signal into a set of elementary waveforms called atoms chosen from an over-complete dictionary in such a way so that they represent the inherent signal structures. A crucial part of MP is the creation of the dictionary which directly affects the results of the decomposition and subsequently the quality of source separation. Selecting an appropriate dictionary could prove a difficult task and an adaptive approach would be appropriate. This work proposes a new MP variant termed guided matching pursuit (GMP) which adds a new pre-processing step into the main sequence of the MP algorithm. The purpose of this step is to perform an analysis of the signal and extract important features, termed guide maps, that are used to create dynamic mini-dictionaries comprising atoms which are expected to correlate well with the underlying signal structures thus leading to focused and more efficient searches around particular supports of the signal. This algorithm is accompanied by a modular and highly flexible MATLAB implementation which is suited to the processing of long duration audio signals. Finally the new algorithm is applied to the source separation of two-channel linear instantaneous mixtures and preliminary testing demonstrates that the performance of GMP is on par with the performance of state of the art systems

Discrete Wavelet Transforms

The discrete wavelet transform (DWT) algorithms have a firm position in processing of signals in several areas of research and industry. As DWT provides both octave-scale frequency and spatial timing of the analyzed signal, it is constantly used to solve and treat more and more advanced problems. The present book: Discrete Wavelet Transforms: Algorithms and Applications reviews the recent progress in discrete wavelet transform algorithms and applications. The book covers a wide range of methods (e.g. lifting, shift invariance, multi-scale analysis) for constructing DWTs. The book chapters are organized into four major parts. Part I describes the progress in hardware implementations of the DWT algorithms. Applications include multitone modulation for ADSL and equalization techniques, a scalable architecture for FPGA-implementation, lifting based algorithm for VLSI implementation, comparison between DWT and FFT based OFDM and modified SPIHT codec. Part II addresses image processing algorithms such as multiresolution approach for edge detection, low bit rate image compression, low complexity implementation of CQF wavelets and compression of multi-component images. Part III focuses watermaking DWT algorithms. Finally, Part IV describes shift invariant DWTs, DC lossless property, DWT based analysis and estimation of colored noise and an application of the wavelet Galerkin method. The chapters of the present book consist of both tutorial and highly advanced material. Therefore, the book is intended to be a reference text for graduate students and researchers to obtain state-of-the-art knowledge on specific applications