[PDF] Audio Source Separation eBook

Author : Shoji Makino
Publisher : Springer
Page : 389 pages
File Size : 30,98 MB
Release : 2018-03-01
Category : Technology & Engineering
ISBN : 3319730312

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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.

Author : Emmanuel Vincent
Publisher : John Wiley & Sons
Page : 517 pages
File Size : 46,89 MB
Release : 2018-10-22
Category : Technology & Engineering
ISBN : 1119279895

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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.

Author : Wang, Wenwu
Publisher : IGI Global
Page : 554 pages
File Size : 50,33 MB
Release : 2010-07-31
Category : Computers
ISBN : 1615209204

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Machine audition is the study of algorithms and systems for the automatic analysis and understanding of sound by machine. It has recently attracted increasing interest within several research communities, such as signal processing, machine learning, auditory modeling, perception and cognition, psychology, pattern recognition, and artificial intelligence. However, the developments made so far are fragmented within these disciplines, lacking connections and incurring potentially overlapping research activities in this subject area. Machine Audition: Principles, Algorithms and Systems contains advances in algorithmic developments, theoretical frameworks, and experimental research findings. This book is useful for professionals who want an improved understanding about how to design algorithms for performing automatic analysis of audio signals, construct a computing system for understanding sound, and learn how to build advanced human-computer interactive systems.

Author : Ganesh R. Naik
Publisher : Springer
Page : 549 pages
File Size : 10,14 MB
Release : 2014-05-21
Category : Technology & Engineering
ISBN : 3642550169

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Blind Source Separation intends to report the new results of the efforts on the study of Blind Source Separation (BSS). The book collects novel research ideas and some training in BSS, independent component analysis (ICA), artificial intelligence and signal processing applications. Furthermore, the research results previously scattered in many journals and conferences worldwide are methodically edited and presented in a unified form. The book is likely to be of interest to university researchers, R&D engineers and graduate students in computer science and electronics who wish to learn the core principles, methods, algorithms and applications of BSS. Dr. Ganesh R. Naik works at University of Technology, Sydney, Australia; Dr. Wenwu Wang works at University of Surrey, UK.

Author : Shoji Makino
Publisher : Springer Science & Business Media
Page : 432 pages
File Size : 47,99 MB
Release : 2005-03-17
Category : Computers
ISBN : 9783540240396

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We live in a noisy world! In all applications (telecommunications, hands-free communications, recording, human-machine interfaces, etc) that require at least one microphone, the signal of interest is usually contaminated by noise and reverberation. As a result, the microphone signal has to be "cleaned" with digital signal processing tools before it is played out, transmitted, or stored. This book is about speech enhancement. Different well-known and state-of-the-art methods for noise reduction, with one or multiple microphones, are discussed. By speech enhancement, we mean not only noise reduction but also dereverberation and separation of independent signals. These topics are also covered in this book. However, the general emphasis is on noise reduction because of the large number of applications that can benefit from this technology. The goal of this book is to provide a strong reference for researchers, engineers, and graduate students who are interested in the problem of signal and speech enhancement. To do so, we invited well-known experts to contribute chapters covering the state of the art in this focused field.

Author : Pierre Comon
Publisher : Academic Press
Page : 856 pages
File Size : 17,57 MB
Release : 2010-02-17
Category : Technology & Engineering
ISBN : 0080884946

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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 as optimization criteria, mathematical tools, the design of numerical algorithms, convolutive mixtures, and time frequency approaches. This Handbook is an ideal reference for university researchers, R&D engineers and graduates wishing to learn the core principles, methods, algorithms, and applications of Blind Source Separation. Covers the principles and major techniques and methods in one book Edited by the pioneers in the field with contributions from 34 of the world’s experts Describes the main existing numerical algorithms and gives practical advice on their design Covers the latest cutting edge topics: second order methods; algebraic identification of under-determined mixtures, time-frequency methods, Bayesian approaches, blind identification under non negativity approaches, semi-blind methods for communications Shows the applications of the methods to key application areas such as telecommunications, biomedical engineering, speech, acoustic, audio and music processing, while also giving a general method for developing applications

Author : Mike E. Davies
Publisher : Springer Science & Business Media
Page : 864 pages
File Size : 34,98 MB
Release : 2007-08-28
Category : Computers
ISBN : 3540744932

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This book constitutes the refereed proceedings of the 7th International Conference on Independent Component Analysis and Blind Source Separation, ICA 2007, held in London, UK, in September 2007. It covers algorithms and architectures, applications, medical applications, speech and signal processing, theory, and visual and sensory processing.

Author : Ben Gold
Publisher : John Wiley & Sons
Page : 684 pages
File Size : 48,71 MB
Release : 2011-08-23
Category : Technology & Engineering
ISBN : 0470195363

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When Speech and Audio Signal Processing published in 1999, it stood out from its competition in its breadth of coverage and its accessible, intutiont-based style. This book was aimed at individual students and engineers excited about the broad span of audio processing and curious to understand the available techniques. Since then, with the advent of the iPod in 2001, the field of digital audio and music has exploded, leading to a much greater interest in the technical aspects of audio processing. This Second Edition will update and revise the original book to augment it with new material describing both the enabling technologies of digital music distribution (most significantly the MP3) and a range of exciting new research areas in automatic music content processing (such as automatic transcription, music similarity, etc.) that have emerged in the past five years, driven by the digital music revolution. New chapter topics include: Psychoacoustic Audio Coding, describing MP3 and related audio coding schemes based on psychoacoustic masking of quantization noise Music Transcription, including automatically deriving notes, beats, and chords from music signals. Music Information Retrieval, primarily focusing on audio-based genre classification, artist/style identification, and similarity estimation. Audio Source Separation, including multi-microphone beamforming, blind source separation, and the perception-inspired techniques usually referred to as Computational Auditory Scene Analysis (CASA).

Author : Shoji Makino
Publisher : Springer Science & Business Media
Page : 439 pages
File Size : 21,52 MB
Release : 2007-09-07
Category : Technology & Engineering
ISBN : 1402064799

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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.

Author : Prathmesh Ravindra Matodkar
Publisher :
Page : 0 pages
File Size : 40,6 MB
Release : 2019
Category : Computer sound processing
ISBN :

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Audio Source Separation is an interesting problem, which gives us the power to separate individual elements that make up a mixture signal and analyze them or use them or different functions ranging from re mixing, mastering or for educational purpose.With different instruments, sounds, timbers interacting with each other, it is difficult to visualize their combination to make the final mixture signal.There were few methods which attempted exploiting the statistical relations of the individual sources with final the final mixture signals.With the arrival of machine learning, neural networks, researchers are curious to know the outcome of applying various deep learning models for solving this problem of audio source separation. The availability of larger memory and processing power has encouraged the use of deep learning methodologies in solving various problems.Their ability find interesting patterns with the introduction of non linearity, convolutions layers, short memory cells has helped achieve better results in the domains of image, video, audio. These models are flexible, hence a model used in one domain can be modified to suite other domains as well. The development of various APIs like Tensorflow, Keras, Theano, Pytorch has made the realization and application of complicated operations involved in deep learning models easy to understand and implement. A song is made up of different sources, instruments. In this thesis our main focus would be to extract bass, drums and vocals from a given song.These three elemnts have distinct timber and also different frequency regions where they have maximum presence.These sources are also the driving force of a song. Different techniques have been used till date to solve this problem.An overview of these techniques, proposed model and the elements included are explained in the chapters ahead.