Model Based Signal Enhancement For Impulse Response Measurement
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Model Based Signal Enhancement for Impulse Response Measurement by Xun Wang Pdf
Impulse response measurements that are performed outdoors are highly susceptible to the uncertainties caused by the non-perfect measurement setup, the presence of background noise, and fluctuations in media such as wind and temperature drift. This work concentrates on two scenarios: the measurement of reflection coefficients of noise barriers and the influence of temperature variances in machinery cavities. Regarding the sound barrier measurement outdoors, a linear four-microphone array can be used to separate direct sound and reflected sound if the sound barrier does not include complicated scattering structures. With regard to the impulse response of an air-borne sound measurement for a machine monitoring system, a time-warping model for inter-period and intra-period temperature variances is investigated.
Uncertainties in Acoustical Transfer Functions by Pascal Dietrich Pdf
Measured transfer functions of acoustic systems are often used to derive single-number parameters. The uncertainty analysis is commonly focused on the derived parameters but not on the transfer function as the primary quantity. This thesis presents an approach to assess the uncertainty contributions in these transfer functions by using analytic models. Uncertainties caused by the measurement method are analyzed with a focus on the underlying signal processing. In particular, the influence of nonlinearities in the acoustic measurement chain are modeled to predict artifacts in the measured signals and hence the calculated acoustic transfer function. Secondly, characterization methods commonly applied in the field of signal processing are linked to the acoustic scenarios and the main influencing parameters. Acoustic parameters are then derived analytically and by means of Monte Carlo simulations considering the uncertainty of these input parameters. In order to provide airborne applications, analytic models for sound barrier and room acoustic measurements are developed incorporating the directivity and the orientation of the sound source as well as the positions of sources and receivers. The simulated uncertainty contributions are validated by measurements. The same approach is also applied to structure-borne sound applications.
Anthropometric Individualization of Head-Related Transfer Functions Analysis and Modeling by Ramona Bomhardt Pdf
Human sound localization helps to pay attention to spatially separated speakers using interaural level and time differences as well as angle-dependent monaural spectral cues. In a monophonic teleconference, for instance, it is much more difficult to distinguish between different speakers due to missing binaural cues. Spatial positioning of the speakers by means of binaural reproduction methods using head-related transfer functions (HRTFs) enhances speech comprehension. These HRTFs are influenced by the torso, head and ear geometry as they describe the propagation path of the sound from a source to the ear canal entrance. Through this geometry-dependency, the HRTF is directional and subject-dependent. To enable a sufficient reproduction, individual HRTFs should be used. However, it is tremendously difficult to measure these HRTFs. For this reason this thesis proposes approaches to adapt the HRTFs applying individual anthropometric dimensions of a user. Since localization at low frequencies is mainly influenced by the interaural time difference, two models to adapt this difference are developed and compared with existing models. Furthermore, two approaches to adapt the spectral cues at higher frequencies are studied, improved and compared. Although the localization performance with individualized HRTFs is slightly worse than with individual HRTFs, it is nevertheless still better than with non-individual HRTFs, taking into account the measurement effort.
Modeling the Radiation of Modern Sound Reinforcement Systems in High Resolution by Stefan Feistel Pdf
Starting from physical theory, this work develops a novel framework for the acoustic simulation of sound radiation by loudspeakers and sound reinforcement systems. First, a theoretical foundation is derived for the accurate description of simple and multi-way loudspeakers using an advanced point-source ''CDPS'' model that incorporates phase data. The model's practical implementation is presented including measurement requirements and the GLL loudspeaker data format specification. In the second part, larger systems are analyzed such as line arrays where the receiver may be located in the near field of the source. It is shown that any extended line source can be modeled accurately after decomposition into smaller CDPS elements. The influence of production variation among elements of an array is investigated and shown to be small. The last part of this work deals with the consequences of fluctuating environmental conditions such as wind and temperature on the coherence of sound signals from multiple sources. A new theoretical model is developed that allows predicting the smooth transition from amplitude to power summation as a function of the statistical properties of the environmental parameters. A part of this work was distinguished with the AES Publications Award 2010. Parts of the proposed data format have been incorporated into the international AES56 standard.
Model-Based Signal Processing by James V. Candy Pdf
A unique treatment of signal processing using a model-based perspective Signal processing is primarily aimed at extracting useful information, while rejecting the extraneous from noisy data. If signal levels are high, then basic techniques can be applied. However, low signal levels require using the underlying physics to correct the problem causing these low levels and extracting the desired information. Model-based signal processing incorporates the physical phenomena, measurements, and noise in the form of mathematical models to solve this problem. Not only does the approach enable signal processors to work directly in terms of the problem's physics, instrumentation, and uncertainties, but it provides far superior performance over the standard techniques. Model-based signal processing is both a modeler's as well as a signal processor's tool. Model-Based Signal Processing develops the model-based approach in a unified manner and follows it through the text in the algorithms, examples, applications, and case studies. The approach, coupled with the hierarchy of physics-based models that the author develops, including linear as well as nonlinear representations, makes it a unique contribution to the field of signal processing. The text includes parametric (e.g., autoregressive or all-pole), sinusoidal, wave-based, and state-space models as some of the model sets with its focus on how they may be used to solve signal processing problems. Special features are provided that assist readers in understanding the material and learning how to apply their new knowledge to solving real-life problems. * Unified treatment of well-known signal processing models including physics-based model sets * Simple applications demonstrate how the model-based approach works, while detailed case studies demonstrate problem solutions in their entirety from concept to model development, through simulation, application to real data, and detailed performance analysis * Summaries provided with each chapter ensure that readers understand the key points needed to move forward in the text as well as MATLAB(r) Notes that describe the key commands and toolboxes readily available to perform the algorithms discussed * References lead to more in-depth coverage of specialized topics * Problem sets test readers' knowledge and help them put their new skills into practice The author demonstrates how the basic idea of model-based signal processing is a highly effective and natural way to solve both basic as well as complex processing problems. Designed as a graduate-level text, this book is also essential reading for practicing signal-processing professionals and scientists, who will find the variety of case studies to be invaluable. An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department
Smart Antennas by T. K. Sarkar,Michael C. Wicks,Magdalena Salazar-Palma,Robert J. Bonneau Pdf
A valuable addition to the Wiley Series in Microwave and Optical Engineering Today's modern wireless mobile communications depend on adaptive "smart" antennas to provide maximum range and clarity. With the recent explosive growth of wireless applications, smart antenna technology has achieved widespread commercial and military applications. The only book available on the topic of adaptive antennas using digital technology, this text reflects the latest developments in smart antenna technology and offers timely information on fundamentals, as well as new adaptive techniques developed by the authors. Coupling electromagnetic aspects of antenna design with signal processing techniques designed to promote accurate and efficient information exchange, the text presents various mechanisms for characterizing signal-path loss associated with signal propagation, particularly for mobile wireless communications systems based on such techniques as joint space-frequency adaptive processing. In clear, accessible language, the authors: * explain the difference between adaptive antennas and adaptive signal processing * Illustrate the procedures for adaptive processing using directive elements in a conformal array * clarify multistage analysis procedure which combines electromagnetic analysis with signal processing * present a survey of the various models for characterizing radio wave propagation in urban and rural environments * describe a method wherein it is possible to identify and eliminate multipath without spatial diversity * optimize the location of base stations in a complex environment The text is an excellent resource for researchers and engineers working in electromagnetics and signal processing who deal with performance improvement of adaptive techniques, as well as those who are concerned with the characterization of propagation channels and applications of airborne phased arrays.
Model-Based Signal Processing by James V. Candy Pdf
Model-Based Signal Processing develops the "model-based approach" to signal processing for a variety of useful model sets including the popularly termed "physics-based" models. It presents a unique viewpoint of signal processing from the model-based perspective
Audio Source Separation and Speech Enhancement by Emmanuel Vincent,Tuomas Virtanen,Sharon Gannot Pdf
Learn the technology behind hearing aids, Siri, and Echo Audio source separation and speech enhancement aim to extract one or more source signals of interest from an audio recording involving several sound sources. These technologies are among the most studied in audio signal processing today and bear a critical role in the success of hearing aids, hands-free phones, voice command and other noise-robust audio analysis systems, and music post-production software. Research on this topic has followed three convergent paths, starting with sensor array processing, computational auditory scene analysis, and machine learning based approaches such as independent component analysis, respectively. This book is the first one to provide a comprehensive overview by presenting the common foundations and the differences between these techniques in a unified setting. Key features: Consolidated perspective on audio source separation and speech enhancement. Both historical perspective and latest advances in the field, e.g. deep neural networks. Diverse disciplines: array processing, machine learning, and statistical signal processing. Covers the most important techniques for both single-channel and multichannel processing. This book provides both introductory and advanced material suitable for people with basic knowledge of signal processing and machine learning. Thanks to its comprehensiveness, it will help students select a promising research track, researchers leverage the acquired cross-domain knowledge to design improved techniques, and engineers and developers choose the right technology for their target application scenario. It will also be useful for practitioners from other fields (e.g., acoustics, multimedia, phonetics, and musicology) willing to exploit audio source separation or speech enhancement as pre-processing tools for their own needs.
Acoustic Signal Processing for Ocean Exploration by J.M.F Moura,Isabel M.G. Lourtie Pdf
Acoustic Signal Processing for Ocean Explortion has two major goals: (i) to present signal processing algorithms that take into account the models of acoustic propagation in the ocean and; (ii) to give a perspective of the broad set of techniques, problems, and applications arising in ocean exploration. The book discusses related issues and problems focused in model based acoustic signal processing methods. Besides addressing the problem of the propagation of acoustics in the ocean, it presents relevant acoustic signal processing methods like matched field processing, array processing, and localization and detection techniques. These more traditional contexts are herein enlarged to include imaging and mapping, and new signal representation models like time/frequency and wavelet transforms. Several applied aspects of these topics, such as the application of acoustics to fisheries, sea floor swath mapping by swath bathymetry and side scan sonar, autonomous underwater vehicles and communications in underwater are also considered.
How to produce images with sound has intrigued engineers and scientists for many years. Bats, whales and dolphins can easily get good mental images with acoustical energy, but humans have little natural ability for obtaining such images. The history of engineering and science, however, is an impressive demonstration that technological solutions can compensate, and then some, for deficiencies of nature in humans. Thus with the proper technology, we too can "see" with sound. Many methods involv ing ultrasonic energy can be employed to enable us to do so. Few of these methods are at all reminiscent of the acoustic systems employed by animals. Pulse-echo, phase-amplitude and amplitude-mapping approaches constitute the conceptual bases for three fundamentally different types of acoustic imaging systems and can be used for categorizing the systems. However, by now systems exist that combine the approaches in such sophisticated ways as to make an unambiguous categorization of some of the more complicated systems difficult or impossible. Among the instruments so far pro duced are mechanically-scanning focused instruments, chirped pulse-echo instruments, and instruments involving holography, tomography, parametric excitation, phase conju gation, neural networks, random phase transduction, finite element methods, Doppler frequency shifting, pseudo inversion, Bragg diffraction and reflection, and a host of other principles. The fifty-five chapters in this volume are selected from papers presented at the Eighteenth International Symposium on Acoustical Imaging which was held in Santa Barbara, California on September 18 - 20, 1989.
Nonlinear System Identification by Stephen A. Billings Pdf
Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains describes a comprehensive framework for the identification and analysis of nonlinear dynamic systems in the time, frequency, and spatio-temporal domains. This book is written with an emphasis on making the algorithms accessible so that they can be applied and used in practice. Includes coverage of: The NARMAX (nonlinear autoregressive moving average with exogenous inputs) model The orthogonal least squares algorithm that allows models to be built term by term where the error reduction ratio reveals the percentage contribution of each model term Statistical and qualitative model validation methods that can be applied to any model class Generalised frequency response functions which provide significant insight into nonlinear behaviours A completely new class of filters that can move, split, spread, and focus energy The response spectrum map and the study of sub harmonic and severely nonlinear systems Algorithms that can track rapid time variation in both linear and nonlinear systems The important class of spatio-temporal systems that evolve over both space and time Many case study examples from modelling space weather, through identification of a model of the visual processing system of fruit flies, to tracking causality in EEG data are all included to demonstrate how easily the methods can be applied in practice and to show the insight that the algorithms reveal even for complex systems NARMAX algorithms provide a fundamentally different approach to nonlinear system identification and signal processing for nonlinear systems. NARMAX methods provide models that are transparent, which can easily be analysed, and which can be used to solve real problems. This book is intended for graduates, postgraduates and researchers in the sciences and engineering, and also for users from other fields who have collected data and who wish to identify models to help to understand the dynamics of their systems.
Maximum-Likelihood Deconvolution by Jerry M. Mendel Pdf
Convolution is the most important operation that describes the behavior of a linear time-invariant dynamical system. Deconvolution is the unraveling of convolution. It is the inverse problem of generating the system's input from knowledge about the system's output and dynamics. Deconvolution requires a careful balancing of bandwidth and signal-to-noise ratio effects. Maximum-likelihood deconvolution (MLD) is a design procedure that handles both effects. It draws upon ideas from Maximum Likelihood, when unknown parameters are random. It leads to linear and nonlinear signal processors that provide high-resolution estimates of a system's input. All aspects of MLD are described, from first principles in this book. The purpose of this volume is to explain MLD as simply as possible. To do this, the entire theory of MLD is presented in terms of a convolutional signal generating model and some relatively simple ideas from optimization theory. Earlier approaches to MLD, which are couched in the language of state-variable models and estimation theory, are unnecessary to understand the essence of MLD. MLD is a model-based signal processing procedure, because it is based on a signal model, namely the convolutional model. The book focuses on three aspects of MLD: (1) specification of a probability model for the system's measured output; (2) determination of an appropriate likelihood function; and (3) maximization of that likelihood function. Many practical algorithms are obtained. Computational aspects of MLD are described in great detail. Extensive simulations are provided, including real data applications.
This book presents the basic concepts of adaptive signal processing and adaptive filtering in a concise and straightforward manner, using clear notations that facilitate actual implementation. Important algorithms are described in detailed tables which allow the reader to verify learned concepts. The book covers the family of LMS and algorithms as well as set-membership, sub-band, blind, IIR adaptive filtering, and more. The book is also supported by a web page maintained by the author.