Fundamental frequency

About: Fundamental frequency is a research topic. Over the lifetime, 8941 publications have been published within this topic receiving 131583 citations.

A study of band saw vibrations

Abstract: Band saw flexural natural frequencies decrease continuously with increasing band velocity at a rate dependent upon the pulley mounting system—minimum if the band is allowed to extend under the dynamic loading with the initial static load held constant, and maximum if the band length is fixed. The Galerkin and the flexible band solutions provide bounding fundamental frequency approximations that are simple and accurate. Small periodic band tension variations may induce a large amplitude equilibrium as in the case of the classical string. However, the most probable occurrence of instability is not the classical string subharmonic (fundamental frequency equals 1 2 tension excitation frequency), but occurs at two other excitation frequencies that depend upon the band velocity and pulley mounting system.

Modeling the identification of concurrent vowels with different fundamental frequencies.

Abstract: Human listeners are better able to identify two simultaneous vowels if the fundamental frequencies of the vowels are different. A computational model is presented which, for the first time, is able to simulate this phenomenon at least qualitatively. The first stage of the model is based upon a bank of bandpass filters and inner hair‐cell simulators that simulate approximately the most relevant characteristics of the human auditory periphery. The output of each filter/hair‐cell channel is then autocorrelated to extract pitch and timbre information. The pooled autocorrelation function (ACF) based on all channels is used to derive a pitch estimate for one of the component vowels from a signal composed of two vowels. Individual channel ACFs showing a pitch peak at this value are combined and used to identify the first vowel using a template matching procedure. The ACFs in the remaining channels are then combined and used to identify the second vowel. Model recognition performance shows a rapid improvement in ...

Large-eddy simulation of a compressible flow past a deep cavity

Abstract: Large-eddy simulations of the flow over a deep cavity are performed. The computations reproduce identically all the parameters of the experiment by Forestier and co-workers [J. Fluid Mech. (to be published)], including the high Reynolds number ReL=8.6×105. Spectra show an accurate prediction of the peak levels of the fundamental frequency and its first harmonics. Results are also analyzed both in terms of Reynolds and phase averages, the procedure used to compute phase averages being identical to the one used during the experiment. Agreement with the experimental data is found to be excellent. The expansion rate of the shear layer is accurately described, and the temporal physics of the flow, including the dynamics of the coherent structures, is fully recovered. By comparison with an auxiliary computation wherein the wind-tunnel upper wall is not taken into account, the cavity is found to oscillate in a flow-acoustic resonance mode. New values for the γ constant of Rossiter’s model are then proposed for a...

Pitch Determination of Speech Signals: Algorithms and Devices

Abstract: 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.

Modeling prosodic dynamics for speaker recognition

Abstract: Most current state-of-the-art automatic speaker recognition systems extract speaker-dependent features by looking at short-term spectral information. This approach ignores long-term information that can convey supra-segmental information, such as prosodics and speaking style. We propose two approaches that use the fundamental frequency and energy trajectories to capture long-term information. The first approach uses bigram models to model the dynamics of the fundamental frequency and energy trajectories for each speaker. The second approach uses the fundamental frequency trajectories of a predefined set of words as the speaker templates and then, using dynamic time warping, computes the distance between the templates and the words from the test message. The results presented in this work are on Switchboard I using the NIST Extended Data evaluation design. We show that these approaches can achieve an equal error rate of 3.7%, which is a 77% relative improvement over a system based on short-term pitch and energy features alone.