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Author: Emmanuel Vincent
Publisher: John Wiley & Sons
ISBN: 1119279895
Category : Technology & Engineering
Languages : en
Pages : 517
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Book Description
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: Emmanuel Vincent
Publisher: John Wiley & Sons
ISBN: 1119279895
Category : Technology & Engineering
Languages : en
Pages : 517
Get Book Here
Book Description
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: Shoji Makino
Publisher: Springer
ISBN: 3319730312
Category : Technology & Engineering
Languages : en
Pages : 389
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Book Description
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: Shoji Makino
Publisher: Springer Science & Business Media
ISBN: 9783540240396
Category : Computers
Languages : en
Pages : 432
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Book Description
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: Patrick A. Naylor
Publisher: Springer Science & Business Media
ISBN: 1849960569
Category : Technology & Engineering
Languages : en
Pages : 388
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Book Description
Speech Dereverberation gathers together an overview, a mathematical formulation of the problem and the state-of-the-art solutions for dereverberation. Speech Dereverberation presents current approaches to the problem of reverberation. It provides a review of topics in room acoustics and also describes performance measures for dereverberation. The algorithms are then explained with mathematical analysis and examples that enable the reader to see the strengths and weaknesses of the various techniques, as well as giving an understanding of the questions still to be addressed. Techniques rooted in speech enhancement are included, in addition to a treatment of multichannel blind acoustic system identification and inversion. The TRINICON framework is shown in the context of dereverberation to be a generalization of the signal processing for a range of analysis and enhancement techniques. Speech Dereverberation is suitable for students at masters and doctoral level, as well as established researchers.
Author: Shoji Makino
Publisher: Springer Science & Business Media
ISBN: 1402064799
Category : Technology & Engineering
Languages : en
Pages : 439
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Book Description
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: Eberhard Hänsler
Publisher: Springer Science & Business Media
ISBN: 354070602X
Category : Technology & Engineering
Languages : en
Pages : 740
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Book Description
Users of signal processing systems are never satis?ed with the system they currently use. They are constantly asking for higher quality, faster perf- mance, more comfort and lower prices. Researchers and developers should be appreciative for this attitude. It justi?es their constant e?ort for improved systems. Better knowledge about biological and physical interrelations c- ing along with more powerful technologies are their engines on the endless road to perfect systems. This book is an impressive image of this process. After “Acoustic Echo 1 and Noise Control” published in 2004 many new results lead to “Topics in 2 Acoustic Echo and Noise Control” edited in 2006 . Today – in 2008 – even morenew?ndingsandsystemscouldbecollectedinthisbook.Comparingthe contributions in both edited volumes progress in knowledge and technology becomesclearlyvisible:Blindmethodsandmultiinputsystemsreplace“h- ble” low complexity systems. The functionality of new systems is less and less limited by the processing power available under economic constraints. The editors have to thank all the authors for their contributions. They cooperated readily in our e?ort to unify the layout of the chapters, the ter- nology, and the symbols used. It was a pleasure to work with all of them. Furthermore, it is the editors concern to thank Christoph Baumann and the Springer Publishing Company for the encouragement and help in publi- ing this book.
Author: Sagar Shah
Publisher:
ISBN: 9781088327920
Category : Biomedical engineering
Languages : en
Pages : 91
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Book Description
Hearing aids, automatic speech recognition (ASR) and many other communication systems work well when there is just one sound source with almost no echo, but their performance degrades in situations where more speakers are talking simultaneously or the reverberation is high. Speech separation and speech enhancement are core problems in the field of audio signal processing. Humans are remarkably capable of focusing their auditory attention on a single sound source within a noisy environment, by de-emphasizing all other voices and interferences in surroundings. This capability comes naturally to us humans. However, speech separation remains a significant challenge for computers. It is challenging for the following reasons: the wide variety of sound type, different mixing environment, and the unclear procedure to distinguish sources, especially for similar sounds. Also, perceiving speech in low signal/noise (SNR) conditions is hard for hearing-impaired listeners. Therefore, the motivation is to advance the speech separation algorithms to improve the intelligibility of noisy speech. Latest technologies aim to empower machines with similar abilities. Recently, the deep neural network methods achieved impressive successes in various problems, including speech enhancement, which the task to separate the clean speech of the noise mixture. Due to the advances in deep learning, speech separation can be viewed as a classification problem and treated as a supervised learning problem. Three main components of speech separation or speech enhancement using deep learning methods are acoustic features, learning machines, and training targets. This work aims to implement a single-channel speech separation and enhancement algorithm utilizing machine learning, deep neural networks (DNNs). An extensive set of speech from different speakers and noise data is collected to train a neural network model that predicts time-frequency masks from noisy and mixture speech signals. The algorithm is tested using various noises and combinations of different speakers. Its performance is evaluated in terms of speech quality and intelligibility. In this thesis, I am proposing a variant of the recurrent neural network, which is GRU (gated recurrent unit) for the speech separation and speech enhancement task. It is a simpler model than the LSTM (long short-term memory), which is used now for the task of speech enhancement and speech separation, consisting of a smaller number of parameters and matching the performance of the speech separation and speech enhancement of LSTM networks.
Author: Yiteng (Arden) Huang
Publisher: Springer Science & Business Media
ISBN: 1402077688
Category : Technology & Engineering
Languages : en
Pages : 375
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Book Description
Audio Signal Processing for Next-Generation Multimedia Communication Systems presents cutting-edge digital signal processing theory and implementation techniques for problems including speech acquisition and enhancement using microphone arrays, new adaptive filtering algorithms, multichannel acoustic echo cancellation, sound source tracking and separation, audio coding, and realistic sound stage reproduction. This book's focus is almost exclusively on the processing, transmission, and presentation of audio and acoustic signals in multimedia communications for telecollaboration where immersive acoustics will play a great role in the near future.
Author: Pierre Comon
Publisher: Academic Press
ISBN: 0080884946
Category : Technology & Engineering
Languages : en
Pages : 856
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Book Description
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: Israel Cohen
Publisher: Springer Science & Business Media
ISBN: 3642111300
Category : Technology & Engineering
Languages : en
Pages : 342
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Book Description
Modern communication devices, such as mobile phones, teleconferencing systems, VoIP, etc., are often used in noisy and reverberant environments. Therefore, signals picked up by the microphones from telecommunication devices contain not only the desired near-end speech signal, but also interferences such as the background noise, far-end echoes produced by the loudspeaker, and reverberations of the desired source. These interferences degrade the fidelity and intelligibility of the near-end speech in human-to-human telecommunications and decrease the performance of human-to-machine interfaces (i.e., automatic speech recognition systems). The proposed book deals with the fundamental challenges of speech processing in modern communication, including speech enhancement, interference suppression, acoustic echo cancellation, relative transfer function identification, source localization, dereverberation, and beamforming in reverberant environments. Enhancement of speech signals is necessary whenever the source signal is corrupted by noise. In highly non-stationary noise environments, noise transients, and interferences may be extremely annoying. Acoustic echo cancellation is used to eliminate the acoustic coupling between the loudspeaker and the microphone of a communication device. Identification of the relative transfer function between sensors in response to a desired speech signal enables to derive a reference noise signal for suppressing directional or coherent noise sources. Source localization, dereverberation, and beamforming in reverberant environments further enable to increase the intelligibility of the near-end speech signal.
