Speech enhancement : theory and practice

Description

Introduction Understanding the Enemy: Noise Classes of Speech Enhancement AlgorithmsBook OrganizationReferences FUNDAMENTALS DISCRETE-TIME SIGNAL PROCESSING AND SHORT-TIME FOURIER ANALYSISDiscrete-Time Signals Linear Time-Invariant Discrete-Time Systems The z-Transform Discrete-Time Fourier Transform Short-Time Fourier Transform Spectrographic Analysis of Speech SignalsSummaryReferences SPEECH PRODUCTION AND PERCEPTIONThe Speech SignalThe Speech Production Process Engineering Model of Speech ProductionClasses of Speech SoundsAcoustic Cues in Speech Perception SummaryReferences NOISE COMPENSATION BY HUMAN LISTENERSIntelligibility of Speech in Multiple-Talker Conditions Acoustic Properties of Speech Contributing to RobustnessPerceptual Strategies for Listening in Noise SummaryReferences ALGORITHMSSPECTRAL-SUBTRACTIVE ALGORITHMSBasic Principles of Spectral SubtractionA Geometric View of Spectral Subtraction Shortcomings of the Spectral Subtraction Method Spectral Subtraction Using Oversubtraction Nonlinear Spectral SubtractionMultiband Spectral Subtraction MMSE Spectral Subtraction Algorithm Extended Spectral SubtractionSpectral Subtraction Using Adaptive Gain Averaging Selective Spectral Subtraction Spectral Subtraction Based on Perceptual Properties Performance of Spectral Subtraction Algorithms Summary References WIENER FILTERINGIntroduction to Wiener Filter TheoryWiener Filters in the Time Domain Wiener Filters in the Frequency Domain Wiener Filters and Linear PredictionWiener Filters for Noise Reduction Iterative Wiener Filtering Imposing Constraints on Iterative Wiener Filtering Constrained Iterative Wiener FilteringConstrained Wiener Filtering Estimating the Wiener Gain FunctionIncorporating Psychoacoustic Constraints in Wiener Filtering Codebook-Driven Wiener FilteringAudible Noise Suppression Algorithm SummaryReferences STATISTICAL-MODEL BASED METHODSMaximum-Likelihood Estimators Bayesian Estimators MMSE EstimatorImprovements to the Decision-directed Approach Elimination of Musical Noise Log-MMSE EstimatorMMSE Estimation of the pth-Power SpectrumMMSE Estimators Based on Non-Gaussian DistributionsMaximum A Posteriori (MAP) EstimatorsGeneral Bayesian Estimators Perceptually Motivated Bayesian Estimators Incorporating Speech Absence Probability in Speech Enhancement Methods for Estimating the A Priori Probability of Speech AbsenceSummaryReferences SUBSPACE ALGORITHMSIntroduction Using SVD for Noise Reduction: Theory SVD-Based Algorithms: White Noise SVD-Based Algorithms: Colored Noise SVD-Based Methods: A Unified View EVD-Based Methods: White Noise EVD-Based Methods: Colored NoiseEVD-Based Methods: A Unified ViewPerceptually Motivated Subspace Algorithms Subspace-Tracking Algorithms SummaryReferences NOISE ESTIMATION ALGORITHMSVoice Activity Detection Vs. Noise EstimationIntroduction to Noise Estimation Algorithms Minimal-Tracking AlgorithmsTime-Recursive Averaging Algorithms for Noise EstimationHistogram-Based TechniquesOther Noise Estimation AlgorithmsObjective Comparison of Noise EstimationAlgorithmsSummaryReferences EVALUATION EVALUATING PERFORMANCE OF SPEECH ENHANCEMENT ALGORITHMS Quality vs. IntelligibilityEvaluating Intelligibility of Processed Speech Evaluating Quality of Processed SpeechEvaluating Reliability of Quality Judgments: Recommended PracticeObjective Quality Measures Nonintrusive Objective Quality MeasuresFigures of Merit of Objective Quality Measures Challenges and Future Directions in Objective Quality Evaluation Summary References COMPARISON OF SPEECH ENHANCEMENT ALGORITHMSNOIZEUS: A Noisy Speech Corpus for Quality Evaluation of Speech Enhancement AlgorithmsComparison of Enhancement Algorithms: Speech Quality Comparison of Enhancement Algorithms: Speech Intelligibility Comparison of Objective Measures for Quality EvaluationSummary References Appendix A: Derivation of the MMSE Estimator Appendix B: Special Functions and Integrals Appendix C: Speech Databases and MATLAB CodeIndex