Source Separation And Machine Learning
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Source Separation And Machine Learning
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Author : Jen-Tzung Chien
language : en
Publisher: Academic Press
Release Date : 2018-10-16
Source Separation And Machine Learning written by Jen-Tzung Chien and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-16 with Technology & Engineering categories.
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
Python Machine Learning Cookbook
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Author : Prateek Joshi
language : en
Publisher: Packt Publishing Ltd
Release Date : 2016-06-23
Python Machine Learning Cookbook written by Prateek Joshi and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-06-23 with Computers categories.
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 techniques Who This Book Is For This book is for Python programmers who are looking to use machine-learning algorithms to create real-world applications. This book is friendly to Python beginners, but familiarity with Python programming would certainly be useful to play around with the code. What You Will Learn Explore classification algorithms and apply them to the income bracket estimation problem Use predictive modeling and apply it to real-world problems Understand how to perform market segmentation using unsupervised learning Explore data visualization techniques to interact with your data in diverse ways Find out how to build a recommendation engine Understand how to interact with text data and build models to analyze it Work with speech data and recognize spoken words using Hidden Markov Models Analyze stock market data using Conditional Random Fields Work with image data and build systems for image recognition and biometric face recognition Grasp how to use deep neural networks to build an optical character recognition system In Detail Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. With this book, you will learn how to perform various machine learning tasks in different environments. We'll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you'll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms. You'll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples. Style and approach You will explore various real-life scenarios in this book where machine learning can be used, and learn about different building blocks of machine learning using independent recipes in the book.
Nonlinear Blind Source Separation And Blind Mixture Identification
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Author : Yannick Deville
language : en
Publisher: Springer Nature
Release Date : 2021-02-02
Nonlinear Blind Source Separation And Blind Mixture Identification written by Yannick Deville and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-02 with Technology & Engineering categories.
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 follow the description of each method but also to see how these methods relate to one another. The coherence of this book also results from the fact that the same notations are used throughout the chapters for the quantities (source signals and so on) that are used in various methods. Finally, among the quite varied types of processing methods that are presented in this book, a significant part of this description is dedicated to methods based on artificial neural networks, especially recurrent ones, which are currently of high interest to the data analysis and machine learning community in general, beyond the more specific signal processing and blind source separation communities.
Audio Source Separation
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Author : Shoji Makino
language : en
Publisher: Springer
Release Date : 2018-03-01
Audio Source Separation written by Shoji Makino and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-01 with Technology & Engineering categories.
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 deep neural network (DNN) techniques, with chapters on multichannel and single channel separation, and a further chapter on DNN based mask estimation for monaural speech separation. In section four, sparse component analysis (SCA) is discussed, with chapters on source separation using audio directional statistics modelling, multi-microphone MMSE-based techniques and diffusion map methods. The book brings together leading researchers to provide tutorial-like and in-depth treatments on major audio source separation topics, with the objective of becoming the definitive source for a comprehensive, authoritative, and accessible treatment. This book is written for graduate students and researchers who are interested in audio source separation techniques based on NMF, DNN and SCA.
Audio Source Separation And Speech Enhancement
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Author : Emmanuel Vincent
language : en
Publisher: John Wiley & Sons
Release Date : 2018-10-22
Audio Source Separation And Speech Enhancement written by Emmanuel Vincent and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-22 with Technology & Engineering categories.
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 with sensor array processing, computational auditory scene analysis, and machine learning based approaches such as independent component analysis, respectively. This book is the first one to provide a comprehensive overview by presenting the common foundations and the differences between these techniques in a unified setting. Key features: Consolidated perspective on audio source separation and speech enhancement. Both historical perspective and latest advances in the field, e.g. deep neural networks. Diverse disciplines: array processing, machine learning, and statistical signal processing. Covers the most important techniques for both single-channel and multichannel processing. This book provides both introductory and advanced material suitable for people with basic knowledge of signal processing and machine learning. Thanks to its comprehensiveness, it will help students select a promising research track, researchers leverage the acquired cross-domain knowledge to design improved techniques, and engineers and developers choose the right technology for their target application scenario. It will also be useful for practitioners from other fields (e.g., acoustics, multimedia, phonetics, and musicology) willing to exploit audio source separation or speech enhancement as pre-processing tools for their own needs.
Blind Speech Separation
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Author : Shoji Makino
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-09-07
Blind Speech Separation written by Shoji Makino and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-09-07 with Technology & Engineering categories.
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.
Eeg Signal Processing And Machine Learning
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Author : Saeid Sanei
language : en
Publisher: John Wiley & Sons
Release Date : 2021-09-23
Eeg Signal Processing And Machine Learning written by Saeid Sanei and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-23 with Technology & Engineering categories.
EEG Signal Processing and Machine Learning Explore cutting edge techniques at the forefront of electroencephalogram research and artificial intelligence from leading voices in the field The newly revised Second Edition of EEG Signal Processing and Machine Learning delivers an inclusive and thorough exploration of new techniques and outcomes in electroencephalogram (EEG) research in the areas of analysis, processing, and decision making about a variety of brain states, abnormalities, and disorders using advanced signal processing and machine learning techniques. The book content is substantially increased upon that of the first edition and, while it retains what made the first edition so popular, is composed of more than 50% new material. The distinguished authors have included new material on tensors for EEG analysis and sensor fusion, as well as new chapters on mental fatigue, sleep, seizure, neurodevelopmental diseases, BCI, and psychiatric abnormalities. In addition to including a comprehensive chapter on machine learning, machine learning applications have been added to almost all the chapters. Moreover, multimodal brain screening, such as EEG-fMRI, and brain connectivity have been included as two new chapters in this new edition. Readers will also benefit from the inclusion of: A thorough introduction to EEGs, including neural activities, action potentials, EEG generation, brain rhythms, and EEG recording and measurement An exploration of brain waves, including their generation, recording, and instrumentation, abnormal EEG patterns and the effects of ageing and mental disorders A treatment of mathematical models for normal and abnormal EEGs Discussions of the fundamentals of EEG signal processing, including statistical properties, linear and nonlinear systems, frequency domain approaches, tensor factorization, diffusion adaptive filtering, deep neural networks, and complex-valued signal processing Perfect for biomedical engineers, neuroscientists, neurophysiologists, psychiatrists, engineers, students and researchers in the above areas, the Second Edition of EEG Signal Processing and Machine Learning will also earn a place in the libraries of undergraduate and postgraduate students studying Biomedical Engineering, Neuroscience and Epileptology.
Machine Intelligence And Signal Processing
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Author : Sonali Agarwal
language : en
Publisher: Springer Nature
Release Date : 2020-02-25
Machine Intelligence And Signal Processing written by Sonali Agarwal and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-25 with Technology & Engineering categories.
This book features selected high-quality research papers presented at the International Conference on Machine Intelligence and Signal Processing (MISP 2019), held at the Indian Institute of Technology, Allahabad, India, on September 7–10, 2019. The book covers the latest advances in the fields of machine learning, big data analytics, signal processing, computational learning theory, and their real-time applications. The topics covered include support vector machines (SVM) and variants like least-squares SVM (LS-SVM) and twin SVM (TWSVM), extreme learning machine (ELM), artificial neural network (ANN), and other areas in machine learning. Further, it discusses the real-time challenges involved in processing big data and adapting the algorithms dynamically to improve the computational efficiency. Lastly, it describes recent developments in processing signals, for instance, signals generated from IoT devices, smart systems, speech, and videos and addresses biomedical signal processing: electrocardiogram (ECG) and electroencephalogram (EEG).
Real World Applications Of Quantum Computers And Machine Intelligence
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Author : Ananth, Christo
language : en
Publisher: IGI Global
Release Date : 2024-12-27
Real World Applications Of Quantum Computers And Machine Intelligence written by Ananth, Christo and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-27 with Computers categories.
The emergence of quantum computing promises a monumental shift in technological capabilities, poised to revolutionize various fields where traditional computing methods may fall short. Quantum computing's potential spans a wide spectrum of applications, from enhancing cryptography to revolutionizing climate modeling and drug discovery. Major corporations are integrating quantum computing into artificial intelligence research, marking a pivotal shift from traditional computing methods. Real-World Applications of Quantum Computers and Machine Intelligence explores practical examples in quantum computing and machine learning for various industry revolutions. By contrasting quantum computing with conventional data mining systems, this book offers insights into the transformative potential of quantum computing, enabling the development of new techniques for real-time problem-solving and innovation. This book covers topics such as deep neural networks, environmental technologies, and machine learning, and is a useful resource for computer engineers, industry professionals, researchers, academicians, scientists, business owners, and healthcare workers.