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ویرایش:
نویسندگان: Richard F. Lyon
سری:
ISBN (شابک) : 9781107007536
ناشر: Cambridge University Press
سال نشر: 2017
تعداد صفحات: 599
زبان: English
فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود)
حجم فایل: 43 مگابایت
در صورت تبدیل فایل کتاب Human and Machine Hearing: Extracting Meaning from Sound به فرمت های PDF، EPUB، AZW3، MOBI و یا DJVU می توانید به پشتیبان اطلاع دهید تا فایل مورد نظر را تبدیل نمایند.
توجه داشته باشید کتاب شنوایی انسان و ماشین: استخراج معنا از صدا نسخه زبان اصلی می باشد و کتاب ترجمه شده به فارسی نمی باشد. وبسایت اینترنشنال لایبرری ارائه دهنده کتاب های زبان اصلی می باشد و هیچ گونه کتاب ترجمه شده یا نوشته شده به فارسی را ارائه نمی دهد.
شنوایی انسان و ماشین اولین کتابی است که به طور جامع نحوه عملکرد شنوایی انسان و نحوه ساخت ماشین هایی برای تجزیه و تحلیل صداها را به همان روشی که مردم انجام می دهند، شرح می دهد. ریچارد اف. لیون با تکیه بر بیش از سی و پنج سال تجربه در تجزیه و تحلیل سیستم های شنوایی و ساختمان، توضیح می دهد که چگونه اکنون می توانیم ماشین هایی با توانایی های نزدیک به انسان در گفتار، موسیقی و سایر حوزه های درک صدا بسازیم. او شنوایی انسان را بر حسب مفاهیم مهندسی توضیح می دهد و چگونگی ترکیب این مفاهیم را در ماشین ها برای طیف گسترده ای از کاربردهای مدرن توضیح می دهد. جزئیات این رویکرد در یک سطح قابل دسترس ارائه شده است تا طیف متنوعی از خوانندگان، از علوم اعصاب گرفته تا مهندسی، به یک درک فنی مشترک ارائه شود. شرح شنوایی به عنوان الگوریتمهای پردازش سیگنال توسط کد منبع باز مربوطه پشتیبانی میشود، که کتاب به عنوان اسناد انگیزشی برای آن عمل میکند.
Human and Machine Hearing is the first book to comprehensively describe how human hearing works and how to build machines to analyze sounds in the same way that people do. Drawing on over thirty-five years of experience in analyzing hearing and building systems, Richard F. Lyon explains how we can now build machines with close-to-human abilities in speech, music, and other sound-understanding domains. He explains human hearing in terms of engineering concepts, and describes how to incorporate those concepts into machines for a wide range of modern applications. The details of this approach are presented at an accessible level, to bring a diverse range of readers, from neuroscience to engineering, to a common technical understanding. The description of hearing as signal-processing algorithms is supported by corresponding open-source code, for which the book serves as motivating documentation.
Contents Foreword Preface Part I Sound Analysis and Representation Overview 1 Introduction 1.1 On Vision and Hearing à la David Marr 1.2 Top-Down versus Bottom-Up Analysis 1.3 The Neuromimetic Approach 1.4 Auditory Images 1.5 The Ear as a Frequency Analyzer? 1.6 The Third Sound 1.7 Sound Understanding and Extraction of Meaning 1.8 Leveraging Techniques from Machine Vision and Machine Learning 1.9 Machine Hearing Systems “by the Book” 2 Theories of Hearing 2.1 A “New” Theory of Hearing 2.2 Newer Theories of Hearing 2.3 Active and Nonlinear Theories of Hearing 2.4 Three Auditory Theories 2.5 The Auditory Image Theory of Hearing 3 On Logarithmic and Power-Law Hearing 3.1 Logarithms and Power Laws 3.2 Log Frequency 3.3 Log Power 3.4 Bode Plots 3.5 Perceptual Mappings 3.6 Constant-Q Analysis 3.7 Use Logarithms with Caution 4 Human Hearing Overview 4.1 Human versus Machine 4.2 Auditory Physiology 4.3 Key Problems in Hearing 4.4 Loudness 4.5 Critical Bands, Masking, and Suppression 4.6 Pitch Perception 4.7 Timbre 4.8 Consonance and Dissonance 4.9 Speech Perception 4.10 Binaural Hearing 4.11 Auditory Streaming 4.12 Nonlinearity 4.13 A Way Forward 5 Acoustic Approaches and Auditory Influence 5.1 Sound, Speech, and Music Modeling 5.2 Short-Time Spectral Analysis 5.3 Smoothing and Transformation of Spectra 5.4 The Source–Filter Model and Homomorphic Signal Processing 5.5 Backing Away from Logarithms 5.6 Auditory Frequency Scales 5.7 Mel-Frequency Cepstrum 5.8 Linear Predictive Coding 5.9 PLP and RASTA 5.10 Auditory Techniques in Automatic Speech Recognition 5.11 Improvements Needed Part II Systems Theory for Hearing 6 Introduction to Linear Systems 6.1 Smoothing: A Good Place to Start 6.2 Linear Time-Invariant Systems 6.3 Filters and Frequencies 6.4 Differential Equations and Homogeneous Solutions 6.5 Impulse Responses 6.6 Causality and Stability 6.7 Convolution 6.8 Eigenfunctions and Transfer Functions 6.9 Frequency Response 6.10 Transforms and Operational Methods 6.11 Rational Functions, and Their Poles and Zeros 6.12 Graphical Computation of Transfer Function Gain and Phase 6.13 Convolution Theorem 6.14 Interconnection of Filters in Cascade, Parallel, and Feedback 6.15 Summary and Next Steps 7 Discrete-Time and Digital Systems 7.1 Simulating Systems in Computers 7.2 Discrete-Time Linear Shift-Invariant Systems 7.3 Impulse Response and Convolution 7.4 Frequency in Discrete-Time Systems 7.5 Z Transform and Its Inverse 7.6 Unit Advance and Unit Delay Operators 7.7 Filters and Transfer Functions 7.8 Sampling and Aliasing 7.9 Mappings from Continuous-Time Systems 7.10 Filter Design 7.11 Digital Filters 7.12 Multiple Inputs and Outputs 7.13 Fourier Analysis and Spectrograms 7.14 Perspective and Further Reading 8 Resonators 8.1 Bandpass Filters 8.2 Four Resonant Systems 8.3 Resonator Frequency Responses 8.4 Resonator Impulse Responses 8.5 The Complex Resonator and the Universal Resonance Curve 8.6 Complex Zeros from a Parallel System 8.7 Keeping It Real 8.8 Digital Resonators 9 Gammatone and Related Filters 9.1 Compound Resonators as Auditory Models 9.2 Multiple Poles 9.3 The Complex Gammatone Filter 9.4 The Real Gammatone Filter 9.5 All-Pole Gammatone Filters 9.6 Gammachirp Filters 9.7 Variable Pole Q 9.8 Noncoincident Poles 9.9 Digital Implementations 10 Nonlinear Systems 10.1 Volterra Series and Other Descriptions 10.2 Essential Nonlinearity 10.3 Hopf Bifurcation 10.4 Distributed Bandpass Nonlinearity 10.5 Response Curves of Nonlinear Systems 10.6 Two-Tone Responses 10.7 Nonlinearity and Aliasing 10.8 Cautions 11 Automatic Gain Control 11.1 Input–Output Level Compression 11.2 Nonlinear Feedback Control 11.3 AGC Compression at Equilibrium 11.4 Multiple Cascaded Variable-Gain Stages 11.5 Gain Control via Damping Control in Cascaded Resonators 11.6 AGC Dynamics 11.7 AGC Loop Stability 11.8 Multiple-Loop AGC 12 Waves in Distributed Systems 12.1 Waves in Uniform Linear Media 12.2 Transfer Functions from Wavenumbers 12.3 Nonuniform Media 12.4 Nonuniform Media as Filter Cascades 12.5 Impulse Responses 12.6 Group Velocity and Group Delay Part III The Auditory Periphery 13 Auditory Filter Models 13.1 What Is an Auditory Filter? 13.2 From Resonance to Gaussian Filters 13.3 Ten Good Properties for Auditory Filter Models 13.4 Representative Auditory Filter Models 13.5 Complications: Time-Varying and Nonlinear Auditory Filters 13.6 Fitting Parameters of Filter Models 13.7 Suppression 13.8 Impulse Responses from Physiological Data 13.9 Summary and Application to Cochlear Models 14 Modeling the Cochlea 14.1 On the Structure of the Cochlea 14.2 The Traveling Wave 14.3 1D, 2D, and 3D Hydrodynamics 14.4 Long Waves, Short Waves, and 2D Models 14.5 Active Micromechanics 14.6 Scaling Symmetry and the Cochlear Map 14.7 Filter-Cascade Cochlear Models 14.8 Outer Hair Cells as Active Gain Elements 14.9 Dispersion Relations from Mechanical Models and Experiments 14.10 Inner Hair Cells as Detectors 14.11 Adaptation to Sound via Efferent Control 14.12 Summary and Further Reading 15 The CARFAC Digital Cochlear Model 15.1 Putting the Pieces Together 15.2 The CARFAC Framework 15.3 Physiological Elements 15.4 Analog and Bidirectional Models 15.5 Open-Source Software 15.6 Detailing the CARFAC 16 The Cascade of Asymmetric Resonators 16.1 The Linear Cochlear Model 16.2 Coupled-Form Filter Realization 17 The Outer Hair Cell 17.1 Multiple Effects in One Mechanism 17.2 The Nonlinear Function 17.3 AGC Effect of DOHC 17.4 Typical Distortion Response Patterns 17.5 Completing the Loop 18 The Inner Hair Cell 18.1 Rectification with a Sigmoid 18.2 Adaptive Hair-Cell Models 18.3 A Digital IHC Model 19 The AGC Loop Filter 19.1 The CARFAC’s AGC Loop 19.2 AGC Filter Structure 19.3 Smoothing Filter Pole–Zero Analysis 19.4 AGC Filter Temporal Response 19.5 AGC Filter Spatial Response 19.6 Time–Space Smoothing with Decimation 19.7 Adapted Behavior 19.8 Binaural or Multi-Ear Operation 19.9 Coupled and Multistage AGC in CARFAC and Other Systems Part IV The Auditory Nervous System 20 Auditory Nerve and Cochlear Nucleus 20.1 From Hair Cells to Nerve Firings 20.2 Tonotopic Organization 20.3 Fine Time Structure in Cochleagrams 20.4 Cell Types in the Cochlear Nucleus 20.5 Inhibition and Other Computation 20.6 Spike Timing Codes 21 The Auditory Image 21.1 Movies of Sound 21.2 History 21.3 Stabilizing the Image 21.4 Triggered Temporal Integration 21.5 Conventional Short-Time Autocorrelation 21.6 Asymmetry 21.7 Computing the SAI 21.8 Pitch and Spectrum 21.9 Auditory Images of Music 21.10 Auditory Images of Speech 21.11 Summary SAI Tracks: Pitchograms 21.12 Cochleagram from SAI 21.13 The Log-Lag SAI 22 Binaural Spatial Hearing 22.1 Rayleigh’s Duplex Theory: Interaural Level and Phase 22.2 Interaural Time and Level Differences 22.3 The Head-Related Transfer Function 22.4 Neural Extraction of Interaural Differences 22.5 The Role of the Cochlear Nucleus and the Trapezoid Body 22.6 Binaural Acoustic Reflex and Gain Control 22.7 The Precedence Effect 22.8 Completing the Model 22.9 Interaural Coherence 22.10 Binaural Applications 23 The Auditory Brain 23.1 Scene Analysis: ASA and CASA 23.2 Attention and Stream Segregation 23.3 Stages in the Brain 23.4 Higher Auditory Pathways 23.5 Prospects Part V Learning and Applications 24 Neural Networks for Machine Learning 24.1 Learning from Data 24.2 The Perceptron 24.3 The Training Phase 24.4 Nonlinearities at the Output 24.5 Nonlinearities at the Input 24.6 Multiple Layers 24.7 Neural Units and Neural Networks 24.8 Training by Error Back-Propagation 24.9 Cost Functions and Regularization 24.10 Multiclass Classifiers 24.11 Neural Network Successes and Failures 24.12 Statistical Learning Theory 24.13 Summary and Perspective 25 Feature Spaces 25.1 Feature Engineering 25.2 Automatic Feature Optimization by Deep Networks 25.3 Bandpass Power and Quadratic Features 25.4 Quadratic Features of Cochlear Filterbank Outputs 25.5 Nonlinearities and Gain Control in Feature Extraction 25.6 Neurally Inspired Feature Extraction 25.7 Sparsification and Winner-Take-All Features 25.8 Which Approach Will Win? 26 Sound Search 26.1 Modeling Sounds 26.2 Ranking Sounds Given Text Queries 26.3 Experiments 26.4 Results 26.5 Conclusions and Followup 27 Musical Melody Matching 27.1 Algorithm 27.2 Experiments 27.3 Discussion 27.4 Summary and Conclusions 28 Other Applications 28.1 Auditory Physiology and Psychoacoustics 28.2 Audio Coding and Compression 28.3 Hearing Aids and Cochlear Implants 28.4 Visible Sound 28.5 Diagnosis 28.6 Speech and Speaker Recognition 28.7 Music Information Retrieval 28.8 Security, Surveillance, and Alarms 28.9 Diarization, Summarization, and Indexing 28.10 Have Fun Bibliography Author Index Subject Index