DOCSLIB.ORG

Chapter 1: Introduction and Background

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Chapter 1: Introduction and Background The Coding of Sound Localization Cues in the Inferior Colliculus by Steven M. Chase A dissertation submitted to Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy Baltimore, Maryland June 2006 © Steven M. Chase 2006 All rights reserved Abstract The auditory system uses three cues to decode sound location: interaural time differences (ITDs), interaural level differences (ILDs), and spectral notches (SNs). Initial processing of these cues is performed in several auditory brainstem nuclei that send projections to neurons of the inferior colliculus (IC). This work addresses how information about these different sound localization cues is integrated into the responses of single neurons of the IC. Virtual space techniques were used to create stimulus sets varying in two sound- localization parameters each. By manipulating pairs of cues within a stimulus set, the relative coding of each cue could be compared. Using a variety of information theoretic methods, the mutual information between the localization cues and the neural response was quantified under the assumption of several different encoding schemes. The results show that the three localization cues are best represented by different codes. ITD information is conveyed by spike rate alone, and is contained only in low frequency neurons. ILD information is best represented by a joint rate/first spike latency code. The coding of SNs changes with the best frequency (BF) of the neuron. Low BF neurons represent SNs by the timing of spikes distributed throughout the response, where the spike times are locked to particular stimulus features. High BF neurons, on the other hand, represent SNs by spike rate and, to a lesser extent, first spike latency. The differential coding of the localization cues suggests that information about multiple cues could be multiplexed onto the responses of single neurons. ii These results have implications for how the localization cues might be integrated into a percept of sound location. The accuracy with which each cue can contribute to the overall location percept changes depending on sound conditions, such as the frequency content of the stimulus, temporal characteristics of the stimulus, or the reverberant qualities of the environment. That the cues are differentially coded in the IC implies that the brain may have access to the individual cues at cortical levels, and the weight with which each cue contributes to the location percept could be tailored to the stimulus, environment, and perceptual task. Advisor Dr. Eric D. Young Professor Biomedical Engineering iii Acknowledgements Of course, I could never have completed this work on my own. My gratitude for the people whose patience, understanding, and insight helped me through this dissertation cannot adequately be expressed in words. I am indebted. My advisor, Eric Young, for giving me the guidance to get me going and the freedom to let me find my own way. Brad May, my second reader, who was always willing to answer questions and discuss results. Dissertation committee: Ed Connor, Xiaoqin Wang, and Kechen Zhang. NEL members, past and present, without whom this would have been a dreary and caffeine poor experience: Mike Anderson, Sharba Bandyopadhyay, Ian Bruce, Shanqing Cai, Kevin Davis, Gulam Emadi, Alon Fishbach, Mike Heinz, Ben Letham, Diana Ma, Lina Reiss, Tessa Ropp, Danilo Scepanovic, Zach Smith, William Tam, Phyllis Taylor, Anita Tilotta, Josh Vogelstein, and Jane Yu. My friends and family, who never admitted boredom when listening to the last result or setback. And finally, Anita Zuberi, who has always been a bottomless well of love and support. iv Table of Contents Title page.............................................................................................................................. i Abstract ...............................................................................................................................ii Acknowledgements ............................................................................................................ iv List of Tables.......................................................................................................x List of Figures .................................................................................................................... xi Chapter 1: Introduction and Background.........................................................1 1.1) Cues for sound localization.................................................................................... 3 1.2) Anatomy and physiology of sound localization nuclei.......................................... 4 1.2.1 – The ITD processing pathway ...................................................................... 4 1.2.2 – The ILD processing pathway ...................................................................... 6 1.2.3 – The SN processing pathway ........................................................................ 6 1.2.4 – Overlaps in the localization cue pathways.................................................. 7 1.3) Previous studies of the inferior colliculus relevant to sound localization ............. 8 1.3.1 – Physiological cell types within the inferior colliculus ................................ 8 1.3.2 – Sound location processing within the inferior colliculus............................ 9 1.4) Characterizing the responses of auditory neurons ............................................... 11 v Chapter 2: Methods................................................................................................. 14 2.1) Surgical procedure ............................................................................................... 14 2.2) Electrode fabrication............................................................................................ 16 2.3) Recording procedure............................................................................................ 17 2.4) Stimulus design.................................................................................................... 19 Chapter 3: Mutual Information and Bias Estimation ................................ 23 3.1) Overview.............................................................................................................. 24 3.2) Applying mutual information to neural encoding 26 3.3) Mutual information in two variables 28 3.3.1 – MI in two stimulus variables ..................................................................... 28 3.3.2 – MI in two response variables .................................................................... 30 3.4) Bounds and bias of mutual information estimates............................................... 31 3.5) Mutual information in spike rate 33 3.5.1 – Rate-based MI and the maximum likelihood estimate .............................. 33 3.5.2 – Debiasing schemes for rate-based MI estimates....................................... 34 3.5.3 – The fill-in method: a new debiasing technique ......................................... 39 3.6) Mutual information in a stimulus decoder 42 3.6.1 – Estimating MI from a hard partition decoder........................................... 43 3.6.2 – Debiasing confusion matrices ................................................................... 44 vi 3.7) Mutual information in first spike latency 47 3.7.1 – The breakdown of first spike latency information..................................... 48 3.7.2 – Kernel density estimates of MI.................................................................. 50 3.7.3 – Binless estimates of MI.............................................................................. 53 3.8) Derivation of the cue-interaction equation, Eqn. 3.13 56 3.9) Derivation of the first spike latency information equation, Eqn. 3.31 57 Chapter 4: Sound localization information encoded in spike rate......... 59 4.1) Overview of the data............................................................................................ 60 4.2) Rate responses to the SN/ILD and ITD/ILD stimulus sets 61 4.2.1 – The representation of single cues.............................................................. 61 4.2.2 – The representation of multiple cues .......................................................... 66 4.3) Responses to the ABI/ILD stimulus set 67 4.3.1 – Information about overall level vs. level differences ................................ 67 4.3.2 – Assessing monaural vs. binaural interactions: the Monaural Index ........ 69 4.4) Discussion of rate coding results 71 4.4.1 – Segregation of localization cue information among the IC cell types ...... 71 4.4.2 – Matching stimulus cues to neurons ........................................................... 73 4.4.3 – Comparison with other studies.................................................................. 74 4.5) Renormalization: correcting for slow drifts in neural excitability 75 vii Chapter 5: Sound localization information recovered by a decoder.... 78 5.1) The spike distance metric decoder....................................................................... 79 5.2) SDM analysis of the coding of individual localization cues 81 5.2.1 – Single-cue information .............................................................................. 81 5.2.2 – Time scale of information.......................................................................... 84 5.2.3 – Frozen vs. random noise ........................................................................... 85 5.3) SDM analysis of the coding of multiple
Load more

Recommended publications
  • The Modeling and Quantification of Rhythmic to Non-Rhythmic
    Graduate Institute of Biomedical Electronics and Bioinformatics College of Electrical Engineering and Computer Science National Taiwan University Doctoral Dissertation The Modeling and Quantification of Rhythmic to Non-rhythmic Phenomenon in Electrocardiography during Anesthesia Author : Yu-Ting Lin Advisor : Jenho Tsao, Ph.D. arXiv:1502.02764v1 [q-bio.NC] 10 Feb 2015 February 2015 \All composite things are not constant. Work hard to gain your own enlightenment." Siddh¯arthaGautama Abstract Variations of instantaneous heart rate appears regularly oscillatory in deeper levels of anesthesia and less regular in lighter levels of anesthesia. It is impossible to observe this \rhythmic-to-non-rhythmic" phenomenon from raw electrocardiography waveform in cur- rent standard anesthesia monitors. To explore the possible clinical value, I proposed the adaptive harmonic model, which fits the descriptive property in physiology, and provides adequate mathematical conditions for the quantification. Based on the adaptive har- monic model, multitaper Synchrosqueezing transform was used to provide time-varying power spectrum, which facilitates to compute the quantitative index: \Non-rhythmic- to-Rhythmic Ratio" index (NRR index). I then used a clinical database to analyze the behavior of NRR index and compare it with other standard indices of anesthetic depth. The positive statistical results suggest that NRR index provides addition clinical infor- mation regarding motor reaction, which aligns with current standard tools. Furthermore, the ability to indicates the noxious stimulation is an additional finding. Lastly, I have proposed an real-time interpolation scheme to contribute my study further as a clinical application. Keywords: instantaneous heart rate; rhythmic-to-non-rhythmic; Synchrosqueezing trans- form; time-frequency analysis; time-varying power spectrum; depth of anesthesia; electro- cardiography Acknowledgements First of all, I would like to thank Professor Jenho Tsao for all thoughtful lessons, discus- sions, and guidance he has provided me in the last five years.
    [Show full text]
  • Time-Frequency Analysis of Time-Varying Signals and Non-Stationary Processes
    Time-Frequency Analysis of Time-Varying Signals and Non-Stationary Processes An Introduction Maria Sandsten 2020 CENTRUM SCIENTIARUM MATHEMATICARUM Centre for Mathematical Sciences Contents 1 Introduction 3 1.1 Spectral analysis history . 3 1.2 A time-frequency motivation example . 5 2 The spectrogram 9 2.1 Spectrum analysis . 9 2.2 The uncertainty principle . 10 2.3 STFT and spectrogram . 12 2.4 Gabor expansion . 14 2.5 Wavelet transform and scalogram . 17 2.6 Other transforms . 19 3 The Wigner distribution 21 3.1 Wigner distribution and Wigner spectrum . 21 3.2 Properties of the Wigner distribution . 23 3.3 Some special signals. 24 3.4 Time-frequency concentration . 25 3.5 Cross-terms . 27 3.6 Discrete Wigner distribution . 29 4 The ambiguity function and other representations 35 4.1 The four time-frequency domains . 35 4.2 Ambiguity function . 39 4.3 Doppler-frequency distribution . 44 5 Ambiguity kernels and the quadratic class 45 5.1 Ambiguity kernel . 45 5.2 Properties of the ambiguity kernel . 46 5.3 The Choi-Williams distribution . 48 5.4 Separable kernels . 52 1 Maria Sandsten CONTENTS 5.5 The Rihaczek distribution . 54 5.6 Kernel interpretation of the spectrogram . 57 5.7 Multitaper time-frequency analysis . 58 6 Optimal resolution of time-frequency spectra 61 6.1 Concentration measures . 61 6.2 Instantaneous frequency . 63 6.3 The reassignment technique . 65 6.4 Scaled reassigned spectrogram . 69 6.5 Other modern techniques for optimal resolution . 72 7 Stochastic time-frequency analysis 75 7.1 Definitions of non-stationary processes .
    [Show full text]
  • On the Use of Time–Frequency Reassignment in Additive Sound Modeling*
    PAPERS On the Use of Time–Frequency Reassignment in Additive Sound Modeling* KELLY FITZ, AES Member AND LIPPOLD HAKEN, AES Member Department of Electrical Engineering and Computer Science, Washington University, Pulman, WA 99164 A method of reassignment in sound modeling to produce a sharper, more robust additive representation is introduced. The reassigned bandwidth-enhanced additive model follows ridges in a time–frequency analysis to construct partials having both sinusoidal and noise characteristics. This model yields greater resolution in time and frequency than is possible using conventional additive techniques, and better preserves the temporal envelope of transient signals, even in modified reconstruction, without introducing new component types or cumbersome phase interpolation algorithms. 0INTRODUCTION manipulations [11], [12]. Peeters and Rodet [3] have developed a hybrid analysis/synthesis system that eschews The method of reassignment has been used to sharpen high-level transient models and retains unabridged OLA spectrograms in order to make them more readable [1], [2], (overlap–add) frame data at transient positions. This to measure sinusoidality, and to ensure optimal window hybrid representation represents unmodified transients per- alignment in the analysis of musical signals [3]. We use fectly, but also sacrifices homogeneity. Quatieri et al. [13] time–frequency reassignment to improve our bandwidth- propose a method for preserving the temporal envelope of enhanced additive sound model. The bandwidth-enhanced short-duration complex acoustic signals using a homoge- additive representation is in some way similar to tradi- neous sinusoidal model, but it is inapplicable to sounds of tional sinusoidal models [4]–[6] in that a waveform is longer duration, or sounds having multiple transient events.
    [Show full text]
  • On the Use of Concentrated Time–Frequency Representations As Input to a Deep Convolutional Neural Network: Application to Non Intrusive Load Monitoring
    entropy Article On the Use of Concentrated Time–Frequency Representations as Input to a Deep Convolutional Neural Network: Application to Non Intrusive Load Monitoring Sarra Houidi 1,2 , Dominique Fourer 1,∗ and François Auger 2 1 Laboratoire IBISC (Informatique, BioInformatique, Systèmes Complexes), EA 4526, University Evry/Paris-Saclay, 91020 Evry Cédex, France; [email protected] 2 Institut de Recherche en Energie Electrique de Nantes Atlantique (IREENA), EA 4642, University of Nantes, 44602 Saint-Nazaire, France; [email protected] * Correspondence: [email protected] Received: 29 July 2020; Accepted: 17 August 2020 ; Published: 19 August 2020 Abstract: Since decades past, time–frequency (TF) analysis has demonstrated its capability to efficiently handle non-stationary multi-component signals which are ubiquitous in a large number of applications. TF analysis us allows to estimate physics-related meaningful parameters (e.g., F0, group delay, etc.) and can provide sparse signal representations when a suitable tuning of the method parameters is used. On another hand, deep learning with Convolutional Neural Networks (CNN) is the current state-of-the-art approach for pattern recognition and allows us to automatically extract relevant signal features despite the fact that the trained models can suffer from a lack of interpretability. Hence, this paper proposes to combine together these two approaches to take benefit of their respective advantages and addresses non-intrusive load monitoring (NILM) which consists of identifying a home electrical appliance (HEA) from its measured energy consumption signal as a “toy” problem. This study investigates the role of the TF representation when synchrosqueezed or not, used as the input of a 2D CNN applied to a pattern recognition task.
    [Show full text]
  • Appendix a Transforms and Sampling
    Appendix A Transforms and Sampling A.1 Introduction Along the book, the pertinent theory elements have been invoked when directly linked with the topic being studied. It seems also convenient to have an appendix with a summary of the essential theory, which could be consulted as reference at any moment. It is clear that the main focus should be put on the Fourier transform, and on sampling. Other transforms are also of interest. Indeed, this appendix could become quite long if all formal mathematics was considered, together with extensions, applications, etc. There are books, which will cited in this appendix, that treat in detail these aspects. Our aim here is more to summarize main points, and to provide reference materials. Tables of common transform pairs are provided by [29]. A.2 The Fourier Transform The origins of the Fourier transform can be dated at 1807. It was included in the Fourier’s book on heat propagation. We would recommend to see [28] in order to grasp the history of harmonic analysis, up to recent times. A recent book on the Fourier transform principles and applications is [11]. Besides it, there are many other books that include detailed expositions of the Fourier trans- form. © Springer Science+Business Media Singapore 2017 533 J.M. Giron-Sierra, Digital Signal Processing with Matlab Examples, Volume 1, Signals and Communication Technology, DOI 10.1007/978-981-10-2534-1 534 Appendix A: Transforms and Sampling A.2.1 Definitions A.2.1.1 Fourier Series Fourier series have the form: ∞ ∞ y(t) = a0 + an cos (n · w0 t) + bn sin(n · w0 t) (A.1) n=1 n=1 This series may not converge.
    [Show full text]
  • Application to Non Intrusive Load Monitoring Sarra Houidi, Dominique Fourer, François Auger
    On the Use of Concentrated Time-Frequency Representations as Input to a Deep Convolutional Neural Network: Application to Non Intrusive Load Monitoring Sarra Houidi, Dominique Fourer, François Auger To cite this version: Sarra Houidi, Dominique Fourer, François Auger. On the Use of Concentrated Time-Frequency Rep- resentations as Input to a Deep Convolutional Neural Network: Application to Non Intrusive Load Monitoring. Entropy, MDPI, 2020, 22 (9), pp.911. 10.3390/e22090911. hal-02917749 HAL Id: hal-02917749 https://hal.archives-ouvertes.fr/hal-02917749 Submitted on 19 Aug 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. entropy Article On the Use of Concentrated Time–Frequency Representations as Input to a Deep Convolutional Neural Network: Application to Non Intrusive Load Monitoring Sarra Houidi 1,2 , Dominique Fourer 1,∗ and François Auger 2 1 Laboratoire IBISC (Informatique, BioInformatique, Systèmes Complexes), EA 4526, University Evry/Paris-Saclay, 91020 Evry Cédex, France; [email protected] 2 Institut de Recherche en Energie Electrique de Nantes Atlantique (IREENA), EA 4642, University of Nantes, 44602 Saint-Nazaire, France; [email protected] * Correspondence: [email protected] Received: 29 July 2020; Accepted: 17 August 2020 ; Published: 19 August 2020 Abstract: Since decades past, time–frequency (TF) analysis has demonstrated its capability to efficiently handle non-stationary multi-component signals which are ubiquitous in a large number of applications.
    [Show full text]
  • Arxiv:2004.00501V1 [Eess.SP] 31 Mar 2020 Timating Blood Pressure [127]
    Current state of nonlinear-type time-frequency analysis and applications to high-frequency biomedical signals Hau-Tieng Wua,b aDepartment of Mathematics and Department of Statistical Science, Duke University, Durham, NC, USA bMathematics Division, National Center for Theoretical Sciences, Taipei, Taiwan Abstract Motivated by analyzing complicated time series, nonlinear-type time-frequency analysis became an active research topic in the past decades. Those developed tools have been applied to various problems. In this article, we review those developed tools and summarize their applications to high-frequency biomedical signals. Keywords: Instantaneous frequency, amplitude modulation, wave-shape function, time-frequency representation. 1. Introduction Recently, complicated high-frequency (multivariate) oscillatory time series has attracted a lot of research interest. This kind of time series is characterized by being composed of multiple oscillatory components with time-varying fea- tures, like frequency, amplitude and oscillatory pattern, and contaminated by various kinds of randomness like noise and artifact. To have a sense of the challenge we encounter, here we present some repre- sentative biomedical signals. In the photoplethysmography (PPG) signal [125], the signal is composed of a respiratory component and a hemodynamic com- ponent. Common goals include calculating oxygen saturation [125], extracting heart and respiratory rate [31] and hence analyzing the heart rate variability (HRV) [91] and even breathing pattern variability (BPV) [13] that represents complicated interaction among various physiological systems [133], and even es- arXiv:2004.00501v1 [eess.SP] 31 Mar 2020 timating blood pressure [127]. In 1(a), the red boxes indicate the respiratory cycles. Another hemodynamic signal is the peripheral venous pressure (PVP) [144].
    [Show full text]
  • Interface and Hardware Component Configuration Guide, Cisco IOS Release 12.2SY
    Interface and Hardware Component Configuration Guide, Cisco IOS Release 12.2SY Americas Headquarters Cisco Systems, Inc. 170 West Tasman Drive San Jose, CA 95134-1706 USA http://www.cisco.com Tel: 408 526-4000 800 553-NETS (6387) Fax: 408 527-0883 THE SPECIFICATIONS AND INFORMATION REGARDING THE PRODUCTS IN THIS MANUAL ARE SUBJECT TO CHANGE WITHOUT NOTICE. ALL STATEMENTS, INFORMATION, AND RECOMMENDATIONS IN THIS MANUAL ARE BELIEVED TO BE ACCURATE BUT ARE PRESENTED WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED. USERS MUST TAKE FULL RESPONSIBILITY FOR THEIR APPLICATION OF ANY PRODUCTS. THE SOFTWARE LICENSE AND LIMITED WARRANTY FOR THE ACCOMPANYING PRODUCT ARE SET FORTH IN THE INFORMATION PACKET THAT SHIPPED WITH THE PRODUCT AND ARE INCORPORATED HEREIN BY THIS REFERENCE. IF YOU ARE UNABLE TO LOCATE THE SOFTWARE LICENSE OR LIMITED WARRANTY, CONTACT YOUR CISCO REPRESENTATIVE FOR A COPY. The Cisco implementation of TCP header compression is an adaptation of a program developed by the University of California, Berkeley (UCB) as part of UCB’s public domain version of the UNIX operating system. All rights reserved. Copyright © 1981, Regents of the University of California. NOTWITHSTANDING ANY OTHER WARRANTY HEREIN, ALL DOCUMENT FILES AND SOFTWARE OF THESE SUPPLIERS ARE PROVIDED “AS IS” WITH ALL FAULTS. CISCO AND THE ABOVE-NAMED SUPPLIERS DISCLAIM ALL WARRANTIES, EXPRESSED OR IMPLIED, INCLUDING, WITHOUT LIMITATION, THOSE OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT OR ARISING FROM A COURSE OF DEALING, USAGE, OR TRADE PRACTICE. IN NO EVENT SHALL CISCO OR ITS SUPPLIERS BE LIABLE FOR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, OR INCIDENTAL DAMAGES, INCLUDING, WITHOUT LIMITATION, LOST PROFITS OR LOSS OR DAMAGE TO DATA ARISING OUT OF THE USE OR INABILITY TO USE THIS MANUAL, EVEN IF CISCO OR ITS SUPPLIERS HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.
    [Show full text]
  • A Fast and Robust Spectrogram Reassignment Method
    mathematics Article A Fast and Robust Spectrogram Reassignment Method Vittoria Bruni 1,2,*, Michela Tartaglione 1 and Domenico Vitulano 2 1 Department of Basic and Applied Sciences for Engineering, University of Rome La Sapienza, via Antonio Scarpa 16, 00161 Rome, Italy; [email protected] 2 Institute for the Applications of Calculus, National Research Council, via dei Taurini 19, 00185 Rome, Italy; [email protected] * Correspondence: [email protected]; Tel.: +39-06-49766648 Received: 17 March 2019; Accepted: 14 April 2019; Published: 19 April 2019 Abstract: The improvement of the readability of time-frequency transforms is an important topic in the field of fast-oscillating signal processing. The reassignment method is often used due to its adaptivity to different transforms and nice formal properties. However, it strongly depends on the selection of the analysis window and it requires the computation of the same transform using three different but well-defined windows. The aim of this work is to provide a simple method for spectrogram reassignment, named FIRST (Fast Iterative and Robust Reassignment Thinning), with comparable or better precision than classical reassignment method, a reduced computational effort, and a near independence of the adopted analysis window. To this aim, the time-frequency evolution of a multicomponent signal is formally provided and, based on this law, only a subset of time-frequency points is used to improve spectrogram readability. Those points are the ones less influenced by interfering components. Preliminary results show that the proposed method can efficiently reassign spectrograms more accurately than the classical method in the case of interfering signal components, with a significant gain in terms of required computational effort.
    [Show full text]
  • Second-Order Time-Reassigned Synchrosqueezing Transform: Application to Draupner Wave Analysis
    Second-order Time-Reassigned Synchrosqueezing Transform: Application to Draupner Wave Analysis Dominique Fourer and Franc¸ois Auger July 23, 2019 Abstract This paper addresses the problem of efficiently jointly representing a non- stationary multicomponent signal in time and frequency. We introduce a novel en- hancement of the time-reassigned synchrosqueezing method designed to compute sharpened and reversible representations of impulsive or strongly modulated sig- nals. After establishing theoretical relations of the new proposed method with our previous results, we illustrate in numerical experiments the improvement brought by our proposal when applied on both synthetic and real-world signals. Our ex- periments deal with an analysis of the Draupner wave record for which we provide pioneered time-frequency analysis results. 1 Introduction Time-frequency and time-scale analysis [1, 2, 3, 4] aim at developing efficient and innovative methods to deal with non-stationary multicomponent signals. Among the common approaches, the Short-Time Fourier Transform (STFT) and the Continuous Wavelet Transform (CWT) [5] are the simplest linear transforms which have been in- tensively applied in various applications such as audio [6], biomedical [7], seismic or radar. Unfortunately, these tools are limited by the Heisenberg-Gabor uncertainty prin- ciple. As a consequence, the resulting representations are blurred with a poor energy concentration and require a trade-off between the accuracy of the time or frequency arXiv:1907.09125v1 [eess.SP] 16 Jul 2019 localization. Another approach, the reassignment method [8, 9] was introduced as a mathematically elegant and efficient solution to improve the readability of a time- frequency representation (TFR). The inconvenience is that reassignment provides non- invertible TFRs which limits its interest to analysis or modeling applications.
    [Show full text]
  • Bearing Estimation Using Double Frequency Reassignment for a Linear Passive Array
    POLISH MARITIME RESEARCH 3 (95) 2017 Vol. 24; pp. 26-35 10.1515/pomr-2017-0087 BEARING ESTIMATION USING DOUBLE FREQUENCY REASSIGNMENT FOR A LINEAR PASSIVE ARRAY Krzysztof Czarnecki Wojciech Leśniak Gdansk University of Technology ABSTRACT The paper demonstrates the use of frequency reassignment for bearing estimation. For this task, signals derived from a linear equispaced passive array are used. The presented method makes use of Fourier transformation based spatial spectrum estimation. It is further developed through the application of two-dimensional reassignment, which leads to obtaining highly concentrated energy distributions in the joint frequency-angle domain and sharp graphical imaging. The introduced method can be used for analysing,a priori, unknown signals of broadband, nonstationary, and/or multicomponent type. For such signals, the direction of arrival is obtained based upon the marginal energy distribution in the angle domain, through searching for arguments of its maxima. In the paper, bearing estimation of three popular types of sonar pulses, including linear and hyperbolic frequency modulated pulses, as well as no frequency modulation at all, is considered. The results of numerical experiments performed in the presence of additive white Gaussian noise are presented and compared to conventional digital sum-delay beamforming performed in the time domain. The root-mean-square error and the peak-to-average power ratio, also known as the crest factor, are introduced in order to estimate, respectively, the accuracy of the methods and the sharpness of the obtained energy distributions in the angle domain. Keywords: Direction of arrival, DOA, instantaneous frequency, short-time Fourier transform, STFT, time-frequency reassignment, intercept and surveillance sonar, crest factor INTRODUCTION and multicomponent signals whose parameters are a priori unknown, as well as by allowing comprehensive graphical Bearing estimation is an important issue in sonar and presentation.
    [Show full text]
  • Bearing Estimation Using Double Frequency Reassignment for a Linear Passive Array
    POLISH MARITIME RESEARCH 3 (95) 2017 Vol. 24; pp. 26-35 10.1515/pomr-2017-0087 BEARING ESTIMATION USING DOUBLE FREQUENCY REASSIGNMENT FOR A LINEAR PASSIVE ARRAY Krzysztof Czarnecki Wojciech Leśniak Gdansk University of Technology ABSTRACT The paper demonstrates the use of frequency reassignment for bearing estimation. For this task, signals derived from a linear equispaced passive array are used. The presented method makes use of Fourier transformation based spatial spectrum estimation. It is further developed through the application of two-dimensional reassignment, which leads to obtaining highly concentrated energy distributions in the joint frequency-angle domain and sharp graphical imaging. The introduced method can be used for analysing,a priori, unknown signals of broadband, nonstationary, and/or multicomponent type. For such signals, the direction of arrival is obtained based upon the marginal energy distribution in the angle domain, through searching for arguments of its maxima. In the paper, bearing estimation of three popular types of sonar pulses, including linear and hyperbolic frequency modulated pulses, as well as no frequency modulation at all, is considered. The results of numerical experiments performed in the presence of additive white Gaussian noise are presented and compared to conventional digital sum-delay beamforming performed in the time domain. The root-mean-square error and the peak-to-average power ratio, also known as the crest factor, are introduced in order to estimate, respectively, the accuracy of the methods and the sharpness of the obtained energy distributions in the angle domain. Keywords: Direction of arrival, DOA, instantaneous frequency, short-time Fourier transform, STFT, time-frequency reassignment, intercept and surveillance sonar, crest factor INTRODUCTION and multicomponent signals whose parameters are a priori unknown, as well as by allowing comprehensive graphical Bearing estimation is an important issue in sonar and presentation.
    [Show full text]