1 Introduction. 2 Models of Speech Production and Hearing. 2.1 Organs of Speech Production. 2.2 Characteristics of Speech Signals. 2.3 Model of Speech Production. 2.4 Anatomy of Hearing. 2.5 Performance of the Auditory Organs. Bibliography. 3 Spectral Transformations. 3.1 Fourier Transform of Continuous Signals. 3.2 Fourier Transform of Discrete Signals. 3.3 Linear Shift Invariant Systems. 3.4 The z-Transform. 3.5 The Discrete Fourier Transform. 3.6 Fast Convolution. 3.7 Cepstral Analysis. Bibliography. 4 Filter Banks for Spectral Analysis and Synthesis. 4.1 Spectral Analysis Using Narrow-Band Filters. 4.2 Polyphase Network Filter Banks. 4.3 QuadratureMirror Filter Banks. Bibliography. 5 Stochastic Signals and Estimation. 5.1 Basic Concepts. 5.2 Expectations andMoments. 5.3 Bivariate Statistics. 5.4 Probability and Information. 5.5 Multivariate Statistics. 5.6 Stochastic Processes. 5.7 Estimation of Statistical Quantities by Time Averages. 5.8 Power Spectral Densities. 5.9 Estimation of the Power Spectral Density. 5.10 Statistical Properties of Speech Signals. 5.11 Statistical Properties of DFT Coe.cients. 5.12 Optimal Estimation. Bibliography. 6 Linear Prediction. 6.1 Vocal TractModels and Short-TermPrediction. 6.2 Optimal Prediction Coe.cients for Stationary Signals. 6.3 Predictor Adaptation. 6.4 Long-TermPrediction. Bibliography. 7 Quantization. 7.1 Analog Samples and Digital Presentation. 7.2 Uniform Quantization. 7.3 Non-uniformQuantization. 7.4 OptimalQuantization. 7.5 Adaptive Quantization. 7.6 Vector Quantization. 7.6.1 Principle. Bibliography. 8 Speech Coding. 8.1 Classi.cation of Speech Coding Algorithms. 8.2 Model-Based Predictive Coding. 8.3 Di.erentialWaveform Coding. 8.4 Parametric Coding. 8.5 Hybrid Coding. 8.6 Adaptive Post.ltering. Bibliography. 9 Error Concealment and Softbit Decoding. 9.1 Hardbit Source Decoding. 9.2 Conventional Error Concealment. 9.3 Softbits and L-Values. 9.4 Softbit Source Decoding (SD). 9.5 Application toModel Parameters. 9.6 Further Improvements. Bibliography. 10 Bandwidth Extension of Speech Signals (BWE). 10.1 Narrowband versusWideband Telephony. 10.2 Speech Coding with Integrated BWE. 10.3 BWE without Auxiliary Transmission. Bibliography. 11 Single and Dual Channel Noise Reduction. 11.1 Introduction. 11.2 LinearMMSE Estimators. 11.3 Speech Enhancement in the DFT Domain. 11.4 Optimal Non-Linear Estimators. 11.5 Joint Optimum Detection and Estimation of Speech. 11.6 Computation of Likelihood Ratios. 11.7 Estimation of the A Priory Probability of Speech Presence. 11.8 VAD and Noise Estimation Techniques. 11.9 Dual-Channel Noise Reduction. Bibliography. 12 Multi-Channel Noise Reduction. 12.1 Introduction. 12.2 Spatial Sampling of Sound Fields. 12.3 Beamforming. 12.4 PerformanceMeasures and Spatial Aliasing. 12.5 Design of Fixed Beamformers. 12.6 Adaptive Beamformers. Bibliography. 13 Acoustic Echo Control. 13.1 The Echo Control Problem. 13.2 Evaluation Criteria. 13.3 TheWiener Solution. 13.4 The LMS and NLMS Algorithm. 13.5 Convergence Analysis and Control of the LMS Algorithm. 13.6 Geometric Projection Interpretation of the NLMS Algorithm. 13.7 The A.ne Projection Algorithm. 13.8 Least-Squares and Recursive Least-Squares Algorithms. 13.9 Block Processing and Frequency-Domain Adaptive Filters. 13.9.1 Block LMS Algorithm. 13.10 Additional Measures for Echo Control. 13.11 Stereophonic Acoustic Echo Control. A Codec Standards. B Speech Quality Assessment. Bibliography.
Forward. Preface. 1 Introduction. 1.1 Introduction. 1.2 RSR over Digital Channels. 1.3 Organization of the Book. 2 Speech Recognition with HMMs. 2.1 Introduction. 2.2 Some General Issues. 2.3 Analysis of Speech Signals. 2.4 Vector Quantization. 2.5 Approaches to ASR. 2.6 Hidden Markov Models. 2.7 Application of HMMs to Speech Recognition. 2.8 Model Adaptation. 2.9 Dealing with Uncertainty. 3 Networks and Degradation. 3.1 Introduction. 3.2 Mobile and Wireless Networks. 3.3 IP Networks. 3.4 The Acoustic Environment. 4 Speech Compression and Architectures for RSR. 4.1 Introduction. 4.2 Speech Coding. 4.3 Recognition from Decoded Speech. 4.4 Recognition from Codec Parameters. 4.5 Distributed Speech Recognition. 4.6 Comparison between NSR and DSR. 5 Robustness Against Transmission Channel Errors. 5.1 Introduction. 5.2 Channel Coding Techniques. 5.3 Error Concealment (EC). 6 Front-end Processing for Robust Feature Extraction. 6.1 Introduction. 6.2 Noise Reduction Techniques. 6.3 Voice Activity Detection. 6.4 Feature Normalization. 7 Standards for Distributed Speech Recognition. 7.1 Introduction. 7.2 Signal Preprocessing. 7.3 Feature Extraction. 7.4 Feature Compression and Encoding. 7.5 Feature Decoding and Postprocessing. A Alternative Representations of the LPC Coefficients. B Basic Digital Modulation Concepts. C Review of Channel Coding Techniques. C.1 Media-independent FEC. C.2 Interleaving. Bibliography. List of Acronyms. Index.
Vectors in 2 or 3 Dimensions provides an introduction to vectors from their very basics. The author has approached the subject from a geometrical standpoint and although applications to mechanics will be pointed out and techniques from linear algebra employed, it is the geometric view which is emphasised throughout.Properties of vectors are initially introduced before moving on to vector algebra and transformation geometry. Vector calculus as a means of studying curves and surfaces in 3 dimensions and the concept of isometry are introduced later, providing a stepping stone to more advanced theories.* Adopts a geometric approach* Develops gradually, building from basics to the concept of isometry and vector calculus* Assumes virtually no prior knowledge* Numerous worked examples, exercises and challenge questions
The Vector-Valued Maximin
Quantitative approximation methods apply in many diverse fields of research-neural networks, wavelets, partial differential equations, probability and statistics, functional analysis, and classical analysis to name just a few. For the first time in book form, Quantitative Approximations provides a thorough account of all of the significant developm
In the book Quantization of Fields, the problems of electromagnetic and gravitational fields quantization are examined. Quantization of an electromagnetic field is carried out in photon space, i.e., in the reference system moving with a light velocity. This reference system accompanies a photon, therefore, it is possible to carry out the display of a photon to receive representation about its form and to investigate its parameters and properties. In photon space, the Schrodinger’s nonlinear equation with logarithmic nonlinearity (which the wave function of a photon obeys) is found. On the basis of this equation, the problem of a material particle and photon interaction in photon space is investigated. It is shown that the interaction of a photon and material particle can be calculated in the closed form in photon space. Such calculations can be carried out only approximately by a method of the perturbations theory in Euclidian spaces. It is shown that during interaction of a photon and electron on the electron surface, there are waves propagating with a light velocity. The problem of a vacuum in the photon space and also multiphoton system in this space is investigated. During the quantization of a gravitational field, Einstein's equation for a field of gravitation as a basis is used. It is assumed that curved space-time (Riemann’s space) is not quantized. Quantization is subjected to an energy-impulse tensor. It is supposed that the curvature of space-time due to the presence of the massive bodies does not create a strength condition in space. The part of corresponding components of an energy-impulse tensor is replaced with quantum sizes by a principle of formation for the quantum mechanics matrix form. On the basis of the quantum form of the gravitational field equation, the solution as a graviton-quantum of a gravitational field is received. It is shown that during the propagation of a graviton near a massive body, there is a pumping of the gravitation field energy in the graviton. Therefore, in the field of a massive body, the graviton is possible to register. When there is distance between the graviton and a massive body, its energy is pumped over back in a gravitation field of a massive body. Therefore, to registering the graviton far from a massive body is problematic. In the book, some standard questions of general relativity – the classical theory of gravitational radiation, the theory of gravitational waves, the Schwarzschild’s theory of the solitary mass field, etc. – are submitted also.
This book presents a consistent development of the kohn-nirenberg type global quantization theory in the setting of graded nilpotent lie groups in terms of their representations. It contains a detailed exposition of related background topics on homogeneous lie groups, nilpotent lie groups, and the analysis of rockland operators on graded lie groups together with their associated sobolev spaces. For the specific example of the heisenberg group the theory is illustrated in detail. In addition, the book features a brief account of the corresponding quantization theory in the setting of compact lie groups. The monograph is the winner of the 2014 ferran sunyer i balaguer prize.
Discover the skill-sets required to implement various approaches to Machine Learning with Python Key Features • Explore unsupervised learning with clustering, autoencoders, restricted Boltzmann machines, and more • Build your own neural network models using modern Python libraries • Practical examples show you how to implement different machine learning and deep learning techniques Book Description Unsupervised learning is about making use of raw, untagged data and applying learning algorithms to it to help a machine predict its outcome. With this book, you will explore the concept of unsupervised learning to cluster large sets of data and analyze them repeatedly until the desired outcome is found using Python. This book starts with the key differences between supervised, unsupervised, and semi-supervised learning. You will be introduced to the best-used libraries and frameworks from the Python ecosystem and address unsupervised learning in both the machine learning and deep learning domains. You will explore various algorithms, techniques that are used to implement unsupervised learning in real-world use cases. You will learn a variety of unsupervised learning approaches, including randomized optimization, clustering, feature selection and transformation, and information theory. You will get hands-on experience with how neural networks can be employed in unsupervised scenarios. You will also explore the steps involved in building and training a GAN in order to process images. By the end of this book, you will have learned the art of unsupervised learning for different real-world challenges. What you will learn • Use cluster algorithms to identify and optimize natural groups of data • Explore advanced non-linear and hierarchical clustering in action • Soft label assignments for fuzzy c-means and Gaussian mixture models • Detect anomalies through density estimation • Perform principal component analysis using neural network models • Create unsupervised models using GANs Who this book is for This book is intended for statisticians, data scientists, machine learning developers, and deep learning practitioners who want to build smart applications by implementing key building block unsupervised learning, and master all the new techniques and algorithms offered in machine learning and deep learning using real-world examples. Some prior knowledge of machine learning concepts and statistics is desirable.
"Integers" is a refereedonline journal devoted to research in the area of combinatorial number theory. It publishes original research articles in combinatorics and number theory. Topics covered by the journal include additive number theory, multiplicative number theory, sequences and sets, extremal combinatorics, Ramsey theory, elementary number theory, classical combinatorial problems, hypergraphs, and probabilistic number theory. Integers also houses a combinatorial games section. This work presents all papers of the 2013 volume in book form.
The book is devoted to the structural analysis of vector and random (or both) valued countably additive measures, and used for integral representations of random fields. The spaces can be Banach or Frechet types. Special attention is given to Bochner's boundedness principle and Grothendieck's representation unifying and simplyfying stochastic integrations. Several stationary aspects, extensions and random currents as well as related multilinear forms are analyzed, whilst numerous new procedures and results are included, and many research areas are opened up which also display the geometric aspects in multi dimensions.
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