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1. Introduction.- 1.1 Voice Source Parameter Measurement and the Speech Signal.- 1.2 A Short Look at the Areas of Application.- 1.3 Organization of the Book.- 2. Basic Terminology. A Short Introduction to Digital Signal Processing.- 2.1 The Simplified Model of Speech Excitation.- 2.2 Digital Signal Processing 1: Signal Representation.- 2.3 Digital Signal Processing 2: Filters.- 2.4 Time-Variant Systems. The Principle of Short-Term Analysis.- 2.5 Definition of the Task. The Linear Model of Speech Production.- 2.6 A First Categorization of Pitch Determination Algorithms (PDAs).- 3. The Human Voice Source.- 3.1 Mechanism of Sound Generation at the Larynx.- 3.2 Operational Modes of the Larynx. Registers.- 3.3 The Glottal Source (Excitation) Signal.- 3.4 The Influence of the Vocal Tract Upon Voice Source Parameters.- 3.5 The Voiceless and the Transient Sources.- 4. Measuring Range, Accuracy, Pitch Perception.- 4.1 The Range of Fundamental Frequency.- 4.2 Pitch Perception. Toward a Redefinition of the Task.- 4.2.1 Pitch Perception: Spectral and Virtual Pitch.- 4.2.2 Toward a Redefinition of the Task.- 4.2.3 Difference Limens for Fundamental-Frequency Change.- 4.3 Measurement Accuracy.- 4.4 Representation of the Pitch Information in the Signal.- 4.5 Calibration and Performance Evaluation of a PDA.- 5. Manual and Instrumental Pitch Determination, Voicing Determination.- 5.1 Manual Pitch Determination.- 5.1.1 Time-Domain Manual Pitch Determination.- 5.1.2 Frequency-Domain Manual Pitch Determination.- 5.2 Pitch Determination Instruments (PDIs).- 5.2.1 Clinical Methods for Larynx Inspection.- 5.2.2 Mechanic PDIs.- 5.2.3 Electric PDIs.- 5.2.4 Ultrasonic PDIs.- 5.2.5 Photoelectric PDIs (Transillumination of the Glottis).- 5.2.6 Comparative Evaluation of PDIs.- 5.3 Voicing Determination - Selected Examples.- 5.3.1 Voicing Determination: Parameters.- 5.3.2 Voicing Determination - Simple Voicing Determination Algo-rithms (VDAs) Combined VDA-PDA Systems.- 5.3.3 Multiparameter VDAs. Voicing Determination by Means of Pattern Recognition Methods.- 5.3.4 Summary and Conclusions.- 6. Time-Domain Pitch Determination.- 6.1 Pitch Determination by Fundamental-Harmonic Extraction.- 6.1.1 The Basic Extractor.- 6.1.2 The Simplest Pitch Determination Device - Low-Pass Filter and Zero (or Threshold) Crossings Analysis Basic Extractor.- 6.1.3 Enhancement of the First Harmonic by Nonlinear Means.- 6.1.4 Manual Preset and Tunable (Adaptive) Filters.- 6.2 The Other Extreme - Temporal Structure Analysis.- 6.2.1 Envelope Modeling - the Analog Approach.- 6.2.2 Simple Peak Detector and Global Correction.- 6.2.3 Zero Crossings and Excursion Cycles.- 6.2.4 Mixed-Feature Algorithms.- 6.2.5 Other PDAs That Investigate the Temporal Structure of the Signal.- 6.3 The Intermediate Device: Temporal Structure Transformation and Simplification.- 6.3.1 Temporal Structure Simplification by Inverse Filtering.- 6.3.2 The Discontinuity in the Excitation Signal: Event Detection.- 6.4 Parallel Processing in Fundamental Period Determination. Multichannel PDAs.- 6.4.1 PDAs with Multichannel Preprocessor Filters.- 6.4.2 PDAs with Several Channels Applying Different Extraction Principles.- 6.5 Special-Purpose (High-Accuracy) Time-Domain PDAs.- 6.5.1 Glottal Inverse Filtering.- 6.5.2 Determining the Instant of Glottal Closure.- 6.6 The Postprocessor.- 6.6.1 Time-to-Frequency Conversion Display.- 6.6.2 f0 Determination With Basic Extractor Omitted.- 6.6.3 Global Error Correction Routines.- 6.6.4 Smoothing Pitch Contours.- 6.7 Final Comments.- 7. Design and Implementation of a Time-Domain PDA for Undistorted and Band-Limited Signals.- 7.1 The Linear Algorithm.- 7.1.1 Prefiltering.- 7.1.2 Measurement and Suppression of F1.- 7.1.3 The Basic Extractor.- 7.1.4 Problems with the Formant F2. Implementation of a Multiple Two-Pulse Filter (TPF).- 7.1.5 Phase Relations and Starting Point of the Period.- 7.1.6 Performance of the Algorithm with Respect to Linear Distortions, Especially to Band Limitations.- 7.2 Band-Limited Signals in Time-Domain PDAs.- 7.2.1 Concept of the Universal PDA.- 7.2.2 Once More: Use of Nonlinear Distortion in Time-Domain PDAs.- 7.3 An Experimental Study Towards a Universal Time-Domain PDA Applying a Nonlinear Function and a Threshold Analysis Basic Extractor.- 7.3.1 Setup of the Experiment.- 7.3.2 Relative Amplitude and Enhancement of First Harmonic.- 7.4 Toward a Choice of Optimal Nonlinear Functions.- 7.4.1 Selection with Respect to Phase Distortions.- 7.4.2 Selection with Respect to Amplitude Characteristics.- 7.4.3 Selection with Respect to the Sequence of Processing.- 7.5 Implementation of a Three-Channel PDA with Nonlinear Processing.- 7.5.1 Selection of Nonlinear Functions.- 7.5.2 Determination of the Parameter for the Comb Filter.- 7.5.3 Threshold Function in the Basic Extractor.- 7.5.4 Selection of the Most Likely Channel in the Basic Extractor.- 8. Short-Term Analysis Pitch Determination.- 8.1 The Short-Term Transformation and Its Consequences.- 8.2 Autocorrelation Pitch Determination.- 8.2.1 The Autocorrelation Function and Its Relation to the Power Spectrum.- 8.2.2 Analog Realizations.- 8.2.3 "Ordinary" Autocorrelation PDAs.- 8.2.4 Autocorrelation PDAs with Nonlinear Preprocessing.- 8.2.5 Autocorrelation PDAs with Linear Adaptive Preprocessing.- 8.3 "Anticorrelation" Pitch Determination: Average Magnitude Difference Function, Distance and Dissimilarity Measures, and Other Nonstationary Short-Term Analysis PDAs.- 8.3.1 Average Magnitude Difference Function (AMDF).- 8.3.2 Generalized Distance Functions.- 8.3.3 Nonstationary Short-Term Analysis and Incremental Time-Domain PDAs.- 8.4 Multiple Spectral Transform ("Cepstrum") Pitch Determination.- 8.4.1 The More General Aspect: Deconvolution.- 8.4.2 Cepstrum Pitch Determination.- 8.5 Frequency-Domain PDAs.- 8.5.1 Spectral Compression: Frequency and Period Histogram Product Spectrum.- 8.5.2 Harmonic Matching. Psychoacoustic PDAs.- 8.5.3 Determination of f0 from the Distance of Adjacent Spectral Peaks.- 8.5.4 The Fast Fourier Transform, Spectral Resolution, and the Computing Effort.- 8.6 Maximum-Likelihood (Least-Squares) Pitch Determination.- 8.6.1 The Least-Squares Algorithm.- 8.6.2 A Multichannel Solution.- 8.6.3 Computing Complexity, Relation to Comb Filters, Simplified Realizations.- 8.7 Summary and Conclusions.- 9. General Discussion: Summary, Error Analysis, Applications.- 9.1 A Short Survey of the Principal Methods of Pitch Determination.- 9.1.1 Categorization of PDAs and Definitions of Pitch.- 9.1.2 The Basic Extractor.- 9.1.3 The Postprocessor.- 9.1.4 Methods of Preprocessing.- 9.1.5 The Impact of Technology of the Design of PDAs and the Question of Computing Effort.- 9.2 Calibration, Search for Standards.- 9.2.1 Data Acquisition.- 9.2.2 Creating the Standard Pitch Contour Manually, Automatically, and by an Interactive PDA.- 9.2.3 Creating a Standard Contour by Means of a PDI.- 9.3 Performance Evaluation of PDAs.- 9.3.1 Comparative Performance Evaluation of PDAs: Some Examples from the Literature.- 9.3.2 Methods of Error Analysis.- 9.4 A Closer Look at the Applications.- 9.4.1 Has the Problem Been Solved?.- 9.4.2 Application in Phonetics, Linguistics, and Musicology.- 9.4.3 Application in Education and in Pathology.- 9.4.4 The "Technical" Application: Speech Communication.- 9.4.5 A Way Around the Problem in Speech Communication: Voice-Excited and Residual-Excited Vocoding (Baseband Coding).- 9.5 Possible Paths Towards a General Solution.- Appendix A. Experimental Data on the Behavior of Nonlinear Functions in Time-Domain Pitch Determination Algorithms.- A.1 The Data Base of the Investigation.- A.2 Examples for the Behavior of the Nonlinear Functions.- A.3 Relative Amplitude RA1 and Enhancement RE1 of the First Harmonic.- A.4 Relative Amplitude RASM of Spurious Maximum and Autocorrelation Threshold.- A.5 Processing Sequence, Preemphasis, Phase, Band Limitation.- A.6 Optimal Performance of Nonlinear Functions.- A.7 Performance of the Comb Filters.- Appendix B. Original Text of the Quotations in Foreign Languages Throughout This Book.- List of Abbreviations.- Author and Subject Index.

Pitch Determination of Speech Signals: Algorithms and Devices by Wolfgang Hess