Source Separation and Machine Learning

Source Separation and Machine Learning presents the fundamentals in adaptive learning algorithms for Blind Source Separation (BSS) and emphasizes the importance of machine learning perspectives. It illustrates how BSS problems are tackled through adaptive learning algorithms and model-based approaches using the latest information on mixture signals to build a BSS model that is seen as a statistical model for a whole system. Looking at different models, including independent component analysis (ICA), nonnegative matrix factorization (NMF), nonnegative tensor factorization (NTF), and deep neural network (DNN), the book addresses how they have evolved to deal with multichannel and single-channel source separation. Emphasizes the modern model-based Blind Source Separation (BSS) which closely connects the latest research topics of BSS and Machine Learning Includes coverage of Bayesian learning, sparse learning, online learning, discriminative learning and deep learning Presents a number of case studies of model-based BSS (categorizing them into four modern models - ICA, NMF, NTF and DNN), using a variety of learning algorithms that provide solutions for the construction of BSS systems

Produk Detail:

  • Author : Jen-Tzung Chien
  • Publisher : Academic Press
  • Pages : 384 pages
  • ISBN : 0128045779
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKSource Separation and Machine Learning

Source Separation and Machine Learning

Source Separation and Machine Learning
  • Author : Jen-Tzung Chien
  • Publisher : Academic Press
  • Release : 01 November 2018
GET THIS BOOKSource Separation and Machine Learning

Source Separation and Machine Learning presents the fundamentals in adaptive learning algorithms for Blind Source Separation (BSS) and emphasizes the importance of machine learning perspectives. It illustrates how BSS problems are tackled through adaptive learning algorithms and model-based approaches using the latest information on mixture signals to build a BSS model that is seen as a statistical model for a whole system. Looking at different models, including independent component analysis (ICA), nonnegative matrix factorization (NMF), nonnegative tensor factorization (NTF), and

Audio Source Separation

Audio Source Separation
  • Author : Shoji Makino
  • Publisher : Springer
  • Release : 01 March 2018
GET THIS BOOKAudio Source Separation

This book provides the first comprehensive overview of the fascinating topic of audio source separation based on non-negative matrix factorization, deep neural networks, and sparse component analysis. The first section of the book covers single channel source separation based on non-negative matrix factorization (NMF). After an introduction to the technique, two further chapters describe separation of known sources using non-negative spectrogram factorization, and temporal NMF models. In section two, NMF methods are extended to multi-channel source separation. Section three introduces

Handbook of Blind Source Separation

Handbook of Blind Source Separation
  • Author : Pierre Comon,Christian Jutten
  • Publisher : Academic Press
  • Release : 17 February 2010
GET THIS BOOKHandbook of Blind Source Separation

Edited by the people who were forerunners in creating the field, together with contributions from 34 leading international experts, this handbook provides the definitive reference on Blind Source Separation, giving a broad and comprehensive description of all the core principles and methods, numerical algorithms and major applications in the fields of telecommunications, biomedical engineering and audio, acoustic and speech processing. Going beyond a machine learning perspective, the book reflects recent results in signal processing and numerical analysis, and includes topics such

Unsupervised Signal Processing

Unsupervised Signal Processing
  • Author : João Marcos Travassos Romano,Romis Attux,Charles Casimiro Cavalcante,Ricardo Suyama
  • Publisher : CRC Press
  • Release : 03 September 2018
GET THIS BOOKUnsupervised Signal Processing

Unsupervised Signal Processing: Channel Equalization and Source Separation provides a unified, systematic, and synthetic presentation of the theory of unsupervised signal processing. Always maintaining the focus on a signal processing-oriented approach, this book describes how the subject has evolved and assumed a wider scope that covers several topics, from well-established blind equalization and source separation methods to novel approaches based on machine learning and bio-inspired algorithms. From the foundations of statistical and adaptive signal processing, the authors explore and elaborate

Audio Source Separation and Speech Enhancement

Audio Source Separation and Speech Enhancement
  • Author : Emmanuel Vincent,Tuomas Virtanen,Sharon Gannot
  • Publisher : John Wiley & Sons
  • Release : 24 July 2018
GET THIS BOOKAudio Source Separation and Speech Enhancement

Learn the technology behind hearing aids, Siri, and Echo Audio source separation and speech enhancement aim to extract one or more source signals of interest from an audio recording involving several sound sources. These technologies are among the most studied in audio signal processing today and bear a critical role in the success of hearing aids, hands-free phones, voice command and other noise-robust audio analysis systems, and music post-production software. Research on this topic has followed three convergent paths, starting

Python Machine Learning Cookbook

Python Machine Learning Cookbook
  • Author : Prateek Joshi
  • Publisher : Packt Publishing Ltd
  • Release : 23 June 2016
GET THIS BOOKPython Machine Learning Cookbook

100 recipes that teach you how to perform various machine learning tasks in the real world About This Book Understand which algorithms to use in a given context with the help of this exciting recipe-based guide Learn about perceptrons and see how they are used to build neural networks Stuck while making sense of images, text, speech, and real estate? This guide will come to your rescue, showing you how to perform machine learning for each one of these using various

Audio Source Separation and Speech Enhancement

Audio Source Separation and Speech Enhancement
  • Author : Emmanuel Vincent,Tuomas Virtanen,Sharon Gannot
  • Publisher : John Wiley & Sons
  • Release : 24 July 2018
GET THIS BOOKAudio Source Separation and Speech Enhancement

Learn the technology behind hearing aids, Siri, and Echo Audio source separation and speech enhancement aim to extract one or more source signals of interest from an audio recording involving several sound sources. These technologies are among the most studied in audio signal processing today and bear a critical role in the success of hearing aids, hands-free phones, voice command and other noise-robust audio analysis systems, and music post-production software. Research on this topic has followed three convergent paths, starting

Latent Variable Analysis and Signal Separation

Latent Variable Analysis and Signal Separation
  • Author : Vincent Vigneron,Vicente Zarzoso,Eric Moreau,Rémi Gribonval,Emmanuel Vincent
  • Publisher : Springer Science & Business Media
  • Release : 27 September 2010
GET THIS BOOKLatent Variable Analysis and Signal Separation

Thisvolumecollectsthepaperspresentedatthe9thInternationalConferenceon Latent Variable Analysis and Signal Separation,LVA/ICA 2010. The conference was organized by INRIA, the French National Institute for Computer Science and Control,and was held in Saint-Malo, France, September 27–30,2010,at the Palais du Grand Large. Tenyearsafterthe?rstworkshoponIndependent Component Analysis(ICA) in Aussois, France, the series of ICA conferences has shown the liveliness of the community of theoreticians and practitioners working in this ?eld. While ICA and blind signal separation have become mainstream topics, new approaches have emerged

Machine Learning Algorithms for Independent Vector Analysis and Blind Source Separation

Machine Learning Algorithms for Independent Vector Analysis and Blind Source Separation
  • Author : In Tae Lee
  • Publisher : Unknown Publisher
  • Release : 28 June 2022
GET THIS BOOKMachine Learning Algorithms for Independent Vector Analysis and Blind Source Separation

Blind signal separation (BSS) aims at recovering unknown source signals from the observed sensor signals where the mixing process is also unknown. As a popular method to solve this problem, independent component analysis (ICA) maximizes the mutual independence among, or equivalently the non-Gaussianity of, the signals and has been very successful especially when the unknown mixing process is instantaneous. In most realistic situations, however, there are time delay and reverberations which involve long filter lengths in the time domain. Such

Speech Separation by Humans and Machines

Speech Separation by Humans and Machines
  • Author : Pierre Divenyi
  • Publisher : Springer Science & Business Media
  • Release : 02 November 2004
GET THIS BOOKSpeech Separation by Humans and Machines

This book is appropriate for those specializing in speech science, hearing science, neuroscience, or computer science and engineers working on applications such as automatic speech recognition, cochlear implants, hands-free telephones, sound recording, multimedia indexing and retrieval.

Speech Enhancement

Speech Enhancement
  • Author : Jacob Benesty,Shoji Makino,Jingdong Chen
  • Publisher : Springer Science & Business Media
  • Release : 30 March 2006
GET THIS BOOKSpeech Enhancement

A strong reference on the problem of signal and speech enhancement, describing the newest developments in this exciting field. The general emphasis is on noise reduction, because of the large number of applications that can benefit from this technology.

Nonlinear Blind Source Separation and Blind Mixture Identification

Nonlinear Blind Source Separation and Blind Mixture Identification
  • Author : Yannick Deville,Leonardo Tomazeli Duarte,Shahram Hosseini
  • Publisher : Springer Nature
  • Release : 02 February 2021
GET THIS BOOKNonlinear Blind Source Separation and Blind Mixture Identification

This book provides a detailed survey of the methods that were recently developed to handle advanced versions of the blind source separation problem, which involve several types of nonlinear mixtures. Another attractive feature of the book is that it is based on a coherent framework. More precisely, the authors first present a general procedure for developing blind source separation methods. Then, all reported methods are defined with respect to this procedure. This allows the reader not only to more easily

Blind Speech Separation

Blind Speech Separation
  • Author : Shoji Makino,Te-Won Lee,Hiroshi Sawada
  • Publisher : Springer Science & Business Media
  • Release : 07 September 2007
GET THIS BOOKBlind Speech Separation

This is the world’s first edited book on independent component analysis (ICA)-based blind source separation (BSS) of convolutive mixtures of speech. This book brings together a small number of leading researchers to provide tutorial-like and in-depth treatment on major ICA-based BSS topics, with the objective of becoming the definitive source for current, comprehensive, authoritative, and yet accessible treatment.

Latent Variable Analysis and Signal Separation

Latent Variable Analysis and Signal Separation
  • Author : Petr Tichavský,Massoud Babaie-Zadeh,Olivier J.J. Michel,Nadège Thirion-Moreau
  • Publisher : Springer
  • Release : 13 February 2017
GET THIS BOOKLatent Variable Analysis and Signal Separation

This book constitutes the proceedings of the 13th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2017, held in Grenoble, France, in Feburary 2017. The 53 papers presented in this volume were carefully reviewed and selected from 60 submissions. They were organized in topical sections named: tensor approaches; from source positions to room properties: learning methods for audio scene geometry estimation; tensors and audio; audio signal processing; theoretical developments; physics and bio signal processing; latent variable analysis in observation sciences; ICA

Advances in Independent Component Analysis and Learning Machines

Advances in Independent Component Analysis and Learning Machines
  • Author : Ella Bingham,Samuel Kaski,Jorma Laaksonen,Jouko Lampinen
  • Publisher : Academic Press
  • Release : 14 May 2015
GET THIS BOOKAdvances in Independent Component Analysis and Learning Machines

In honour of Professor Erkki Oja, one of the pioneers of Independent Component Analysis (ICA), this book reviews key advances in the theory and application of ICA, as well as its influence on signal processing, pattern recognition, machine learning, and data mining. Examples of topics which have developed from the advances of ICA, which are covered in the book are: A unifying probabilistic model for PCA and ICA Optimization methods for matrix decompositions Insights into the FastICA algorithm Unsupervised deep