الصفحة 1
الصفحة 1
Nonlinear Analyses and Algorithms for Speech Processing ; International Conference on Non-Linear Speech Processing, NOLISP 2005, Barcelona, Spain, April 19-22, 2005, Revised Selected Papers
We present in this volume the collection of ?nally accepted papers of NOLISP 2005 conference. It has been the third event in a series of events related to N- linear speech processing, in the framework of the European COST action 277 “Nonlinear speech processing”. Many speci?cs of the speech signal are not well addressed by conv- tional models currently used in the ?eld of speech processing. The purpose of NOLISP is to present and discuss novel ideas, work and results related to alternative techniques for speech processing, which depart from mainstream approaches. With this intention in mind, we provide an open forum for discussion. Alt- nate approaches are appreciated, although the results achieved at present may not clearly surpass results based on state-of-the-art methods. The call for papers was launched at the beginning of 2005, addressing the following domains: 1. Non-Linear Approximation and Estimation 2. Non-Linear Oscillators and Predictors 3. Higher-Order Statistics 4. Independent Component Analysis 5. Nearest Neighbors 6. Neural Networks 7. Decision Trees 8. Non-Parametric Models 9. Dynamics of Non-Linear Systems 10. Fractal Methods 11. Chaos Modeling 12. Non-Linear Di?erential Equations 13. Others All the main ?elds of speech processing are targeted by the workshop, namely: 1. Speech Coding:Thebit rateavailablefor speechsignalsmustbe strictly l- ited in order to accommodate the constraints of the channel resource.
Independent component analysis and signal separation ; 7th International Conference, ICA 2007, London, UK, September 9-12, 2007, Proceedings
Independent Component Analysis and Signal Separation has applications at the intersection of many science and engineering disciplinesconcernedwithunderstandingandextractingusefulinformationfrom data as diverse as neuronal activity and brain images, bioinformatics, com- nications, the World Wide Web, audio, video, sensor signals, or time series.
Independent Component Analysis and Blind Signal Separation ; 6th International Conference, ICA 2006, Charleston, SC, USA, March 5-8, 2006, Proceedings
This book constitutes the refereed proceedings of the 6th International Conference on Independent Component Analysis and Blind Source Separation, ICA 2006, held in Charleston, SC, USA, in March 2006. The 120 revised papers presented were carefully reviewed and selected from 183 submissions. The papers are organized in topical sections on algorithms and architectures, applications, medical applications, speech and signal processing, theory, and visual and sensory processing.
Kernel Based Algorithms for Mining Huge Data Sets : Supervised, Semi-supervised, and Unsupervised Learning
"Kernel Based Algorithms for Mining Huge Data Sets" is the first book treating the fields of supervised, semi-supervised and unsupervised machine learning collectively. The book presents both the theory and the algorithms for mining huge data sets by using support vector machines (SVMs) in an iterative way. It demonstrates how kernel based SVMs can be used for dimensionality reduction (feature elimination) and shows the similarities and differences between the two most popular unsupervised techniques, the principal component analysis (PCA) and the independent component analysis (ICA).
Blind Speech Separation
This is the first book to provide a cutting edge reference to the fascinating topic of blind source separation (BSS) for convolved speech mixtures. Through contributions by the foremost experts on the subject, the book provides an up-to-date account of research findings, explains the underlying theory, and discusses potential applications. The individual chapters are designed to be tutorial in nature with specific emphasis on an in-depth treatment of state of the art techniques. Blind Speech Separation is divided into three parts:Part 1 presents overdetermined or critically determined BSS. Here the main technology is independent component analysis (ICA). ICA is a statistical method for extracting mutually independent sources from their mixtures. Part 2 addresses underdetermined BSS, where there are fewer microphones than source signals. Here, the sparseness of speech sources is very useful; we can utilize time-frequency diversity, where sources are active in different regions of the time-frequency plane.Part 3 presents monaural BSS where there is only one microphone. Here, we can separate a mixture by using the harmonicity and temporal structure of the sources. We can build a probabilistic framework by assuming a source model, and separate a mixture by maximizing the a posteriori probability of the sources.
Algorithm collections for digital signal processing applications using matlab
The Algorithms such as SVD, Eigen decomposition, Gaussian Mixture Model, HMM etc. are scattered in different fields. There is the need to collect all such algorithms for quick reference. Also there is the need to view such algorithms in application point of view. Algorithm Collections for Digital Signal Processing Applications using MATLAB attempts to satisfy the above requirement. Also the algorithms are made clear using MATLAB programs.
