Blind Time Frequency Analysis For Source Discrimination In Multisensor Array Processing
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| Author | : |
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| Total Pages | : 0 |
| Release | : 1998 |
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| ISBN | : |
This report includes results on the applications of time frequency distributions in blind source separation and direction finding problems. A novel approach based on time-frequency distributions (TFDs) for separating signals received by a multiple antenna array is developed. The sources have different time-frequency signatures and are instantaneously mixed at the array sensors. The proposed approach provides a significant improvement in performance over the recently introduced spatial time-frequency distributions, specifically for signals with close time-frequency signatures. Spatial averaging of the TFDs of the sensor data is performed to eliminate the interactions of the sources signals in the time-frequency domain, and as such restores important properties of the source TFD matrix. We have also improved nonstationary source signal estimation by performing the blind source separation using ambiguity functions, and as such, avoid the inclusion of cross-terms in the estimation process. Our third contribution to this area is the introduction of the Time-Frequency MUSIC as a new array signal processing method based on time-frequency signal representations. This report also includes contributions to problems of fast computation TFDs, spatial processing for frequency diversity spread spectrum communications as applied to partial jamming mitigation, and subband array processing to combat fading and multipath.
| Author | : Leon Cohen |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 135 |
| Release | : 2013-03-09 |
| Genre | : Science |
| ISBN | : 1475728387 |
Recent Developments in Time-Frequency Analysis brings together in one place important contributions and up-to-date research results in this fast moving area. Recent Developments in Time-Frequency Analysis serves as an excellent reference, providing insight into some of the most challenging research issues in the field.
| Author | : |
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| Total Pages | : 2304 |
| Release | : 1997 |
| Genre | : Electrical engineering |
| ISBN | : |
| Author | : Shoji Makino |
| Publisher | : Springer |
| Total Pages | : 389 |
| Release | : 2018-03-01 |
| Genre | : Technology & Engineering |
| ISBN | : 3319730312 |
This book provides the first comprehensive overview of the fascinating topic of audio source separation based on non-negative matrix factorization, deep neural networks, and sparse component analysis. The first section of the book covers single channel source separation based on non-negative matrix factorization (NMF). After an introduction to the technique, two further chapters describe separation of known sources using non-negative spectrogram factorization, and temporal NMF models. In section two, NMF methods are extended to multi-channel source separation. Section three introduces deep neural network (DNN) techniques, with chapters on multichannel and single channel separation, and a further chapter on DNN based mask estimation for monaural speech separation. In section four, sparse component analysis (SCA) is discussed, with chapters on source separation using audio directional statistics modelling, multi-microphone MMSE-based techniques and diffusion map methods. The book brings together leading researchers to provide tutorial-like and in-depth treatments on major audio source separation topics, with the objective of becoming the definitive source for a comprehensive, authoritative, and accessible treatment. This book is written for graduate students and researchers who are interested in audio source separation techniques based on NMF, DNN and SCA.
| Author | : Andrzej Cichocki |
| Publisher | : John Wiley & Sons |
| Total Pages | : 500 |
| Release | : 2009-07-10 |
| Genre | : Science |
| ISBN | : 9780470747285 |
This book provides a broad survey of models and efficient algorithms for Nonnegative Matrix Factorization (NMF). This includes NMF’s various extensions and modifications, especially Nonnegative Tensor Factorizations (NTF) and Nonnegative Tucker Decompositions (NTD). NMF/NTF and their extensions are increasingly used as tools in signal and image processing, and data analysis, having garnered interest due to their capability to provide new insights and relevant information about the complex latent relationships in experimental data sets. It is suggested that NMF can provide meaningful components with physical interpretations; for example, in bioinformatics, NMF and its extensions have been successfully applied to gene expression, sequence analysis, the functional characterization of genes, clustering and text mining. As such, the authors focus on the algorithms that are most useful in practice, looking at the fastest, most robust, and suitable for large-scale models. Key features: Acts as a single source reference guide to NMF, collating information that is widely dispersed in current literature, including the authors’ own recently developed techniques in the subject area. Uses generalized cost functions such as Bregman, Alpha and Beta divergences, to present practical implementations of several types of robust algorithms, in particular Multiplicative, Alternating Least Squares, Projected Gradient and Quasi Newton algorithms. Provides a comparative analysis of the different methods in order to identify approximation error and complexity. Includes pseudo codes and optimized MATLAB source codes for almost all algorithms presented in the book. The increasing interest in nonnegative matrix and tensor factorizations, as well as decompositions and sparse representation of data, will ensure that this book is essential reading for engineers, scientists, researchers, industry practitioners and graduate students across signal and image processing; neuroscience; data mining and data analysis; computer science; bioinformatics; speech processing; biomedical engineering; and multimedia.
| Author | : Shoji Makino |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 432 |
| Release | : 2005-03-17 |
| Genre | : Computers |
| ISBN | : 9783540240396 |
We live in a noisy world! In all applications (telecommunications, hands-free communications, recording, human-machine interfaces, etc) that require at least one microphone, the signal of interest is usually contaminated by noise and reverberation. As a result, the microphone signal has to be "cleaned" with digital signal processing tools before it is played out, transmitted, or stored. This book is about speech enhancement. Different well-known and state-of-the-art methods for noise reduction, with one or multiple microphones, are discussed. By speech enhancement, we mean not only noise reduction but also dereverberation and separation of independent signals. These topics are also covered in this book. However, the general emphasis is on noise reduction because of the large number of applications that can benefit from this technology. The goal of this book is to provide a strong reference for researchers, engineers, and graduate students who are interested in the problem of signal and speech enhancement. To do so, we invited well-known experts to contribute chapters covering the state of the art in this focused field.
| Author | : Pierre Comon |
| Publisher | : Academic Press |
| Total Pages | : 856 |
| Release | : 2010-02-17 |
| Genre | : Technology & Engineering |
| ISBN | : 0080884946 |
Edited by the people who were forerunners in creating the field, together with contributions from 34 leading international experts, this handbook provides the definitive reference on Blind Source Separation, giving a broad and comprehensive description of all the core principles and methods, numerical algorithms and major applications in the fields of telecommunications, biomedical engineering and audio, acoustic and speech processing. Going beyond a machine learning perspective, the book reflects recent results in signal processing and numerical analysis, and includes topics such as optimization criteria, mathematical tools, the design of numerical algorithms, convolutive mixtures, and time frequency approaches. This Handbook is an ideal reference for university researchers, R&D engineers and graduates wishing to learn the core principles, methods, algorithms, and applications of Blind Source Separation. Covers the principles and major techniques and methods in one book Edited by the pioneers in the field with contributions from 34 of the world’s experts Describes the main existing numerical algorithms and gives practical advice on their design Covers the latest cutting edge topics: second order methods; algebraic identification of under-determined mixtures, time-frequency methods, Bayesian approaches, blind identification under non negativity approaches, semi-blind methods for communications Shows the applications of the methods to key application areas such as telecommunications, biomedical engineering, speech, acoustic, audio and music processing, while also giving a general method for developing applications
| Author | : Mads Christensen |
| Publisher | : Springer Nature |
| Total Pages | : 141 |
| Release | : 2022-06-01 |
| Genre | : Technology & Engineering |
| ISBN | : 303102558X |
Periodic signals can be decomposed into sets of sinusoids having frequencies that are integer multiples of a fundamental frequency. The problem of finding such fundamental frequencies from noisy observations is important in many speech and audio applications, where it is commonly referred to as pitch estimation. These applications include analysis, compression, separation, enhancement, automatic transcription and many more. In this book, an introduction to pitch estimation is given and a number of statistical methods for pitch estimation are presented. The basic signal models and associated estimation theoretical bounds are introduced, and the properties of speech and audio signals are discussed and illustrated. The presented methods include both single- and multi-pitch estimators based on statistical approaches, like maximum likelihood and maximum a posteriori methods, filtering methods based on both static and optimal adaptive designs, and subspace methods based on the principles of subspace orthogonality and shift-invariance. The application of these methods to analysis of speech and audio signals is demonstrated using both real and synthetic signals, and their performance is assessed under various conditions and their properties discussed. Finally, the estimators are compared in terms of computational and statistical efficiency, generalizability and robustness. Table of Contents: Fundamentals / Statistical Methods / Filtering Methods / Subspace Methods / Amplitude Estimation
| Author | : Saeed V. Vaseghi |
| Publisher | : Vieweg+Teubner Verlag |
| Total Pages | : 424 |
| Release | : 1996-05 |
| Genre | : Technology & Engineering |
| ISBN | : |
Bayesian Estimation and classification. Hidden markov models. Wiener filters. Kalman and adaptive least squared error filters.
| Author | : Panicos A. Kyriacou |
| Publisher | : Academic Press |
| Total Pages | : 508 |
| Release | : 2021-11-03 |
| Genre | : Technology & Engineering |
| ISBN | : 012823525X |
Photoplethysmography: Technology, Signal Analysis, and Applications is the first comprehensive volume on the theory, principles, and technology (sensors and electronics) of photoplethysmography (PPG). It provides a detailed description of the current state-of-the-art technologies/optical components enabling the extreme miniaturization of such sensors, as well as comprehensive coverage of PPG signal analysis techniques including machine learning and artificial intelligence. The book also outlines the huge range of PPG applications in healthcare, with a strong focus on the contribution of PPG in wearable sensors and PPG for cardiovascular assessment. Presents the underlying principles and technology surrounding PPG Includes applications for healthcare and wellbeing Focuses on PPG in wearable sensors and devices Presents advanced signal analysis techniques Includes cutting-edge research, applications and future directions
