Journal of the Audio Engineering Society
| Author | : Audio Engineering Society |
| Publisher | : |
| Total Pages | : 788 |
| Release | : 2008 |
| Genre | : Acoustical engineering |
| ISBN | : |
"Directory of members" published as pt. 2 of Apr. 1954- issue.
Sound Source Separation And Speech Enhancement Using A Modified Adress Algorithm With Applications In Mobile Communications
Download Sound Source Separation And Speech Enhancement Using A Modified Adress Algorithm With Applications In Mobile Communications full books in PDF, epub, and Kindle. Read online free Sound Source Separation And Speech Enhancement Using A Modified Adress Algorithm With Applications In Mobile Communications ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Speech Enhancement
| Author | : Shoji Makino |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 432 |
| Release | : 2005 |
| Genre | : Hearing |
| ISBN | : 9783540240396 |
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. TOC:Introduction.- Study of the Wiener Filter for Noise Reduction.- Statistical Methods for the Enhancement of Noisy Speech.- Single- und Multi-Microphone Spectral Amplitude Estimation Using a Super-Gaussian Speech Model.- From Volatility Modeling of Financial Time-Series to Stochastic Modeling and Enhancement of Speech Signals.- Single-Microphone Noise Suppression for 3G Handsets Based on Weighted Noise Estimation.- Signal Subspace Techniques for Speech Enhancement.- Speech Enhancement: Application of the Kalman Filter in the Estimate-Maximize (EM) Framework.- Speech Distortion Weighted Multichannel Wiener Filtering Techniques for Noise Reduction.- Adpative Microphone Arrays Employing Spatial Quadratic Soft Constraints and Spectral Shaping.- Single-Microphone Blind Dereverberation.- Separation and Dereverberation of Speech Signals with Multiple Microphones.- Frequency-Domain Blind Source Separation.- Subband Based Blind Source Separation.- Real-Time Blind Source Separation for Moving Speech Signals.- Separation of Speech by Computational Auditory Scene Analysis
Audio Source Separation
| Author | : Shoji Makino |
| Publisher | : Springer |
| Total Pages | : 389 |
| Release | : 2018-03-01 |
| Genre | : Technology & Engineering |
| ISBN | : 3319730312 |
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
| Author | : Emmanuel Vincent |
| Publisher | : John Wiley & Sons |
| Total Pages | : 517 |
| Release | : 2018-10-22 |
| Genre | : Technology & Engineering |
| ISBN | : 1119279895 |
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.
Introduction to Digital Speech Processing
| Author | : Lawrence R. Rabiner |
| Publisher | : Now Publishers Inc |
| Total Pages | : 212 |
| Release | : 2007 |
| Genre | : Computers |
| ISBN | : 1601980701 |
Provides the reader with a practical introduction to the wide range of important concepts that comprise the field of digital speech processing. Students of speech research and researchers working in the field can use this as a reference guide.
Backpropagation
| Author | : Yves Chauvin |
| Publisher | : Psychology Press |
| Total Pages | : 576 |
| Release | : 2013-02-01 |
| Genre | : Psychology |
| ISBN | : 1134775814 |
Composed of three sections, this book presents the most popular training algorithm for neural networks: backpropagation. The first section presents the theory and principles behind backpropagation as seen from different perspectives such as statistics, machine learning, and dynamical systems. The second presents a number of network architectures that may be designed to match the general concepts of Parallel Distributed Processing with backpropagation learning. Finally, the third section shows how these principles can be applied to a number of different fields related to the cognitive sciences, including control, speech recognition, robotics, image processing, and cognitive psychology. The volume is designed to provide both a solid theoretical foundation and a set of examples that show the versatility of the concepts. Useful to experts in the field, it should also be most helpful to students seeking to understand the basic principles of connectionist learning and to engineers wanting to add neural networks in general -- and backpropagation in particular -- to their set of problem-solving methods.
Speech Enhancement
| Author | : Philipos C. Loizou |
| Publisher | : CRC Press |
| Total Pages | : 715 |
| Release | : 2013-02-25 |
| Genre | : Technology & Engineering |
| ISBN | : 1466599227 |
With the proliferation of mobile devices and hearing devices, including hearing aids and cochlear implants, there is a growing and pressing need to design algorithms that can improve speech intelligibility without sacrificing quality. Responding to this need, Speech Enhancement: Theory and Practice, Second Edition introduces readers to the basic pr
Robust Automatic Speech Recognition
| Author | : Jinyu Li |
| Publisher | : Academic Press |
| Total Pages | : 308 |
| Release | : 2015-10-30 |
| Genre | : Technology & Engineering |
| ISBN | : 0128026162 |
Robust Automatic Speech Recognition: A Bridge to Practical Applications establishes a solid foundation for automatic speech recognition that is robust against acoustic environmental distortion. It provides a thorough overview of classical and modern noise-and reverberation robust techniques that have been developed over the past thirty years, with an emphasis on practical methods that have been proven to be successful and which are likely to be further developed for future applications.The strengths and weaknesses of robustness-enhancing speech recognition techniques are carefully analyzed. The book covers noise-robust techniques designed for acoustic models which are based on both Gaussian mixture models and deep neural networks. In addition, a guide to selecting the best methods for practical applications is provided.The reader will: - Gain a unified, deep and systematic understanding of the state-of-the-art technologies for robust speech recognition - Learn the links and relationship between alternative technologies for robust speech recognition - Be able to use the technology analysis and categorization detailed in the book to guide future technology development - Be able to develop new noise-robust methods in the current era of deep learning for acoustic modeling in speech recognition - The first book that provides a comprehensive review on noise and reverberation robust speech recognition methods in the era of deep neural networks - Connects robust speech recognition techniques to machine learning paradigms with rigorous mathematical treatment - Provides elegant and structural ways to categorize and analyze noise-robust speech recognition techniques - Written by leading researchers who have been actively working on the subject matter in both industrial and academic organizations for many years
Techniques for Noise Robustness in Automatic Speech Recognition
| Author | : Tuomas Virtanen |
| Publisher | : John Wiley & Sons |
| Total Pages | : 514 |
| Release | : 2012-11-28 |
| Genre | : Technology & Engineering |
| ISBN | : 1119970881 |
Automatic speech recognition (ASR) systems are finding increasing use in everyday life. Many of the commonplace environments where the systems are used are noisy, for example users calling up a voice search system from a busy cafeteria or a street. This can result in degraded speech recordings and adversely affect the performance of speech recognition systems. As the use of ASR systems increases, knowledge of the state-of-the-art in techniques to deal with such problems becomes critical to system and application engineers and researchers who work with or on ASR technologies. This book presents a comprehensive survey of the state-of-the-art in techniques used to improve the robustness of speech recognition systems to these degrading external influences. Key features: Reviews all the main noise robust ASR approaches, including signal separation, voice activity detection, robust feature extraction, model compensation and adaptation, missing data techniques and recognition of reverberant speech. Acts as a timely exposition of the topic in light of more widespread use in the future of ASR technology in challenging environments. Addresses robustness issues and signal degradation which are both key requirements for practitioners of ASR. Includes contributions from top ASR researchers from leading research units in the field
