Edifici per la città di domani : ambiente e spazio dell'innovazione tecnologica / Ferdinando Semboloni

• Record Nr.: UNICAMPANIASUN0018040
• Autore: Semboloni, Ferdinando
• Pubbl/distr/stampa: Firenze : Alinea, 1991
• Descrizione fisica: 111 p. : ill ; 27 cm.
• Lingua di pubblicazione: Italiano
• Formato: Materiale a stampa
• Livello bibliografico: Monografia

Digital Speech Transmission and Enhancement

• Record Nr.: UNINA9910829806303321
• Autore: Vary Peter
• Pubbl/distr/stampa: Newark : , : John Wiley & Sons, Incorporated, , 2023; ©2024
• ISBN: 1-119-06099-0; 1-119-06097-4
• Edizione: [2nd ed.]
• Descrizione fisica: 1 online resource (595 pages)
• Collana: IEEE Press Series
• Altri autori (Persone): MartinRainer
• Disciplina: 006.454
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Cover -- Title Page -- Copyright -- Contents -- Preface -- Chapter 1 Introduction -- Chapter 2 Models of Speech Production and Hearing -- 2.1 Sound Waves -- 2.2 Organs of Speech Production -- 2.3 Characteristics of Speech Signals -- 2.4 Model of Speech Production -- 2.4.1 Acoustic Tube Model of the Vocal Tract -- 2.4.2 Discrete Time All‐Pole Model of the Vocal Tract -- 2.5 Anatomy of Hearing -- 2.6 Psychoacoustic Properties of the Auditory System -- 2.6.1 Hearing and ; Loudness -- 2.6.2 Spectral Resolution -- 2.6.3 Masking -- 2.6.4 Spatial Hearing -- 2.6.4.1 Head‐Related Impulse Responses and Transfer Functions -- 2.6.4.2 Law of The First Wavefront -- References -- Chapter 3 Spectral Transformations -- 3.1 Fourier Transform of Continuous Signals -- 3.2 Fourier Transform of Discrete Signals -- 3.3 Linear Shift Invariant Systems -- 3.3.1 Frequency Response of LSI Systems -- 3.4 The z‐transform -- 3.4.1 Relation to Fourier Transform -- 3.4.2 Properties of the ROC -- 3.4.3 Inverse z‐Transform -- 3.4.4 z‐Transform Analysis of LSI Systems -- 3.5 The Discrete Fourier Transform -- 3.5.1 Linear and Cyclic Convolution -- 3.5.2 The DFT of Windowed Sequences -- 3.5.3 Spectral Resolution and Zero Padding -- 3.5.4 The Spectrogram -- 3.5.5 Fast Computation of the DFT: The FFT -- 3.5.6 Radix‐2 Decimation‐in‐Time FFT -- 3.6 Fast Convolution -- 3.6.1 Fast Convolution of Long Sequences -- 3.6.2 Fast Convolution by Overlap‐Add -- 3.6.3 Fast Convolution by Overlap‐Save -- 3.7 Analysis-Modification-Synthesis Systems -- 3.8 Cepstral Analysis -- 3.8.1 Complex Cepstrum -- 3.8.2 Real Cepstrum -- 3.8.3 Applications of the Cepstrum -- 3.8.3.1 Construction of Minimum‐Phase Sequences -- 3.8.3.2 Deconvolution by Cepstral Mean Subtraction -- 3.8.3.3 Computation of the Spectral Distortion Measure -- 3.8.3.4 Fundamental Frequency Estimation -- References.; Chapter 4 Filter Banks for Spectral Analysis and Synthesis -- 4.1 Spectral Analysis Using Narrowband Filters -- 4.1.1 Short‐Term Spectral Analyzer -- 4.1.2 Prototype Filter Design for the Analysis Filter Bank -- 4.1.3 Short‐Term Spectral Synthesizer -- 4.1.4 Short‐Term Spectral Analysis and Synthesis -- 4.1.5 Prototype Filter Design for the Analysis-Synthesis filter bank -- 4.1.6 Filter Bank Interpretation of the DFT -- 4.2 Polyphase Network Filter Banks -- 4.2.1 PPN Analysis Filter Bank -- 4.2.2 PPN Synthesis Filter Bank -- 4.3 Quadrature Mirror Filter Banks -- 4.3.1 Analysis-Synthesis Filter Bank -- 4.3.2 Compensation of Aliasing and Signal Reconstruction -- 4.3.3 Efficient Implementation -- 4.4 Filter Bank Equalizer -- 4.4.1 The Reference Filter Bank -- 4.4.2 Uniform Frequency Resolution -- 4.4.3 Adaptive Filter Bank Equalizer: Gain Computation -- 4.4.3.1 Conventional Spectral Subtraction -- 4.4.3.2 Filter Bank Equalizer -- 4.4.4 Non‐uniform Frequency Resolution -- 4.4.5 Design Aspects &amp -- Implementation -- References -- Chapter 5 Stochastic Signals and Estimation -- 5.1 Basic Concepts -- 5.1.1 Random Events and Probability -- 5.1.2 Conditional Probabilities -- 5.1.3 Random Variables -- 5.1.4 Probability Distributions and Probability Density Functions -- 5.1.5 Conditional PDFs -- 5.2 Expectations and Moments -- 5.2.1 Conditional Expectations and Moments -- 5.2.2 Examples -- 5.2.2.1 The Uniform Distribution -- 5.2.2.2 The Gaussian Density -- 5.2.2.3 The Exponential Density -- 5.2.2.4 The Laplace Density -- 5.2.2.5 The Gamma Density -- 5.2.2.6 χ2‐Distribution -- 5.2.3 Transformation of a Random Variable -- 5.2.4 Relative Frequencies and Histograms -- 5.3 Bivariate Statistics -- 5.3.1 Marginal Densities -- 5.3.2 Expectations and Moments -- 5.3.3 Uncorrelatedness and Statistical Independence -- 5.3.4 Examples of Bivariate PDFs.; 5.3.4.1 The Bivariate Uniform Density -- 5.3.4.2 The Bivariate Gaussian Density -- 5.3.5 Functions of Two Random Variables -- 5.4 Probability and Information -- 5.4.1 Entropy -- 5.4.2 Kullback-Leibler Divergence -- 5.4.3 Cross‐Entropy -- 5.4.4 Mutual Information -- 5.5 Multivariate Statistics -- 5.5.1 Multivariate Gaussian Distribution -- 5.5.2 Gaussian Mixture Models -- 5.6 Stochastic Processes -- 5.6.1 Stationary Processes -- 5.6.2 Auto‐Correlation and Auto‐Covariance Functions -- 5.6.3 Cross‐Correlation and Cross‐Covariance Functions -- 5.6.4 Markov Processes -- 5.6.5 Multivariate Stochastic Processes -- 5.7 ; Estimation of Statistical Quantities by Time Averages -- 5.7.1 Ergodic Processes -- 5.7.2 Short‐Time Stationary Processes -- 5.8 Power Spectrum and its Estimation -- 5.8.1 White Noise -- 5.8.2 The Periodogram -- 5.8.3 Smoothed Periodograms -- 5.8.3.1 Non Recursive Smoothing in Time -- 5.8.3.2 Recursive Smoothing in Time -- 5.8.3.3 Log‐Mel Filter Bank Features -- 5.8.4 Power Spectra and Linear Shift‐Invariant Systems -- 5.9 Statistical Properties of Speech Signals -- 5.10 Statistical Properties of DFT Coefficients -- 5.10.1 Asymptotic Statistical Properties -- 5.10.2 Signal‐Plus‐Noise Model -- 5.10.3 Statistics of DFT Coefficients for Finite Frame Lengths -- 5.11 Optimal Estimation -- 5.11.1 MMSE Estimation -- 5.11.2 Estimation of Discrete Random Variables -- 5.11.3 Optimal Linear Estimator -- 5.11.4 The Gaussian Case -- 5.11.5 Joint Detection and Estimation -- 5.12 Non‐Linear Estimation with Deep Neural Networks -- 5.12.1 Basic Network Components -- 5.12.1.1 The Perceptron -- 5.12.1.2 Convolutional Neural Network -- 5.12.2 Basic DNN Structures -- 5.12.2.1 Fully‐Connected Feed‐Forward Network -- 5.12.2.2 Autoencoder Networks -- 5.12.2.3 Recurrent Neural Networks -- 5.12.2.4 Time Delay, Wavenet, and Transformer Networks.; 5.12.2.5 Training of Neural Networks -- 5.12.2.6 Stochastic Gradient Descent (SGD) -- 5.12.2.7 Adaptive Moment Estimation Method (ADAM) -- References -- Chapter 6 Linear Prediction -- 6.1 Vocal Tract Models and Short‐Term Prediction -- 6.1.1 All‐Zero Model -- 6.1.2 All‐Pole Model -- 6.1.3 Pole‐Zero Model -- 6.2 Optimal Prediction Coefficients for Stationary Signals -- 6.2.1 Optimum Prediction -- 6.2.2 Spectral Flatness Measure -- 6.3 Predictor Adaptation -- 6.3.1 Block‐Oriented Adaptation -- 6.3.1.1 Auto‐Correlation Method -- 6.3.1.2 Covariance Method -- 6.3.1.3 Levinson-Durbin Algorithm -- 6.3.2 Sequential Adaptation -- 6.4 Long‐Term Prediction -- References -- Chapter 7 Quantization -- 7.1 Analog Samples and Digital Representation -- 7.2 Uniform Quantization -- 7.3 Non‐uniform Quantization -- 7.4 Optimal Quantization -- 7.5 Adaptive Quantization -- 7.6 Vector Quantization -- 7.6.1 Principle -- 7.6.2 The Complexity Problem -- 7.6.3 Lattice Quantization -- 7.6.4 Design of Optimal Vector Code Books -- 7.6.5 Gain-Shape Vector Quantization -- 7.7 Quantization of the Predictor Coefficients -- 7.7.1 Scalar Quantization of the LPC Coefficients -- 7.7.2 Scalar Quantization of the Reflection Coefficients -- 7.7.3 Scalar Quantization of the LSF Coefficients -- References -- Chapter 8 Speech Coding -- 8.1 Speech‐Coding Categories -- 8.2 Model‐Based Predictive Coding -- 8.3 Linear Predictive Waveform Coding -- 8.3.1 First‐Order DPCM -- 8.3.2 Open‐Loop and Closed‐Loop Prediction -- 8.3.3 Quantization of the Residual Signal -- 8.3.3.1 Quantization with Open‐Loop Prediction -- 8.3.3.2 Quantization with Closed‐Loop Prediction -- 8.3.3.3 Spectral Shaping of the Quantization Error -- 8.3.4 ADPCM with Sequential Adaptation -- 8.4 Parametric Coding -- 8.4.1 Vocoder Structures -- 8.4.2 LPC Vocoder -- 8.5 Hybrid Coding -- 8.5.1 Basic Codec Concepts.; 8.5.1.1 Scalar Quantization of the Residual Signal -- 8.5.1.2 Vector Quantization of the Residual Signal -- 8.5.2 Residual Signal Coding: RELP -- 8.5.3 Analysis by Synthesis: CELP -- 8.5.3.1 Principle -- 8.5.3.2 Fixed Code Book -- 8.5.3.3 Long‐Term Prediction, Adaptive Code Book -- 8.6 Adaptive Postfiltering -- 8.7 Speech Codec Standards: Selected Examples -- 8.7.1 GSM Full‐Rate Codec -- 8.7.2 EFR Codec -- 8.7.3 Adaptive Multi‐Rate Narrowband Codec (AMR‐NB) -- 8.7.4 ITU‐T/G.722: 7 kHz Audio Coding within 64 kbit/s -- 8.7.5 Adaptive Multi‐Rate Wideband Codec (AMR‐WB) -- 8.7.6 Codec for Enhanced Voice Services (EVS) -- 8.7.7 Opus Codec IETF RFC 6716 -- References -- Chapter 9 Concealment of Erroneous or Lost Frames -- ; 9.1 Concepts for Error Concealment -- 9.1.1 Error Concealment by Hard Decision Decoding -- 9.1.2 Error Concealment by Soft Decision Decoding -- 9.1.3 Parameter Estimation -- 9.1.3.1 MAP Estimation -- 9.1.3.2 MS Estimation -- 9.1.4 The A Posteriori Probabilities -- 9.1.4.1 The A Priori Knowledge -- 9.1.4.2 The Parameter Distortion Probabilities -- 9.1.5 Example: Hard Decision vs. Soft Decision -- 9.2 Examples of Error Concealment Standards -- 9.2.1 Substitution and Muting of Lost Frames -- 9.2.2 AMR Codec: Substitution and Muting of Lost Frames -- 9.2.3 EVS Codec: Concealment of Lost Packets -- 9.3 Further Improvements -- References -- Chapter 10 Bandwidth Extension of Speech Signals -- 10.1 BWE Concepts -- 10.2 BWE using the Model of Speech Production -- 10.2.1 Extension of the Excitation Signal -- 10.2.2 Spectral Envelope Estimation -- 10.2.2.1 Minimum Mean Square Error Estimation -- 10.2.2.2 Conditional Maximum A Posteriori Estimation -- 10.2.2.3 Extensions -- 10.2.2.4 Simplifications -- 10.2.3 Energy Envelope Estimation -- 10.3 Speech Codecs with Integrated BWE -- 10.3.1 BWE in the GSM Full‐Rate Codec.; 10.3.2 BWE in the AMR Wideband Codec.