Author: Jang Chyuan Jenq
Publisher:
ISBN: 9783330822221
Category :
Languages : en
Pages :

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Author: Eberhard Hänsler
Publisher: John Wiley & Sons
ISBN: 0471678392
Category : Science
Languages : en
Pages : 474

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Book Description
Authors are well known and highly recognized by the "acoustic echo and noise community." Presents a detailed description of practical methods to control echo and noise Develops a statistical theory for optimal control parameters and presents practical estimation and approximation methods

Author: Eberhard Hänsler
Publisher: Springer Science & Business Media
ISBN: 3540332138
Category : Technology & Engineering
Languages : en
Pages : 648

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Book Description
This book treats important topics in "Acoustic Echo and Noise Control" and reports the latest developments. Methods for enhancing the quality of transmitted speech signals are gaining growing attention in universities and in industrial development laboratories. This book, written by an international team of highly qualified experts, concentrates on the modern and advanced methods.

Author: Robert W. Stewart (Electrical engineer)
Publisher:
ISBN:
Category :
Languages : en
Pages : 118

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Author: Hao Zhang
Publisher:
ISBN:
Category : Adaptive signal processing
Languages : en
Pages : 0

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Book Description
Acoustic echo cancellation (AEC) and active noise control (ANC) have attracted increasing attention in research and industrial applications over the past few decades. Conventionally, AEC and ANC are addressed using methods that are based on adaptive signal processing with the least mean square algorithm as the foundation. They are linear systems and do not perform satisfactorily in the presence of nonlinear distortions. However, nonlinear distortions are inevitable in applications of AEC and ANC due to the limited quality of electronic devices such as amplifiers and loudspeakers. Considering the capacity of deep learning in modeling complex nonlinear relationships, we propose deep learning approaches to address AEC and ANC problems in this dissertation. Different from traditional signal processing methods, we formulate AEC as deep learning based speech separation. The proposed approach, called deep AEC, suppresses echo and noise by separating the near-end speech from a microphone signal with the accessible far-end signal as additional information. Our study of deep AEC starts with magnitude-domain estimation, and a recurrent neural network with bidirectional long short-term memory (BLSTM) is trained to estimate a spectral magnitude mask (SMM) from the microphone and far-end signals. Later, a convolutional recurrent network (CRN) is utilized for complex spectral mapping and results in better speech quality. In addition, we explore combining deep learning based and traditional AEC algorithms to further improve AEC performance. Although deep AEC produces significant improvements over traditional AEC methods, there exists a tradeoff between echo suppression and near-end speech quality. To address this, we propose a neural cascade architecture to leverage the advantages of magnitude-domain and complex-domain estimation. The proposed cascade architecture consists of two modules. A CRN is employed in the first module for complex spectral mapping. The output is then fed as an additional input to the second module, where a long short-term memory network (LSTM) is utilized for magnitude mask estimation. The entire architecture is trained in an end-to-end manner with the two modules optimized jointly using a single loss function. This cascade architecture enables deep AEC to obtain robust magnitude estimation as well as phase enhancement. Modern communication devices are usually equipped with multiple microphones and loudspeakers. Building on deep learning based AEC in the single-channel setup, we then investigate multi-channel AEC (MCAEC) and propose a deep learning based approach named deep MCAEC. We find that the deep MCAEC approach avoids the intrinsic non-uniqueness problem in traditional MCAEC algorithms. For MCAEC setup with multiple microphones, combining deep MCAEC with supervised beamforming further improves AEC performance. For ANC, we formulate it as a supervised learning problem for the first time and propose a deep learning approach, called deep ANC, to address the nonlinear ANC problem. The main idea is to employ deep learning to encode the optimal control parameters corresponding to different noises and environments. We start with a frequency-domain method and train a CRN to estimate the real and imaginary spectrograms of the canceling signal from the reference signal so that the corresponding anti-noise can eliminate or attenuate the primary noise in the ANC system. Deep ANC is a fixed-parameter ANC approach and large-scale multi-condition training is key to achieving good generalization and robustness against a variety of noises. The proposed approach outperforms traditional ANC methods, exhibits unique advantages, and can be trained to achieve active noise cancellation no matter whether the reference signal is noise or noisy speech. The latter property could dramatically expand the scope of ANC applicability. Processing latency is a critical issue for ANC due to the causality constraint of ANC systems. Deep ANC is a frequency-domain block-based method, which incurs an algorithmic delay determined by the frame size. This delay may violate the causality constraint of ANC systems and is considered as a shortcoming of frequency-domain ANC algorithms. To address this, a time-domain method using a self-attending recurrent neural network is proposed, which allows for implementing deep ANC with smaller frame sizes. Augmented with a delay-compensated training strategy and a revised overlap-add method, the algorithmic latency of deep ANC is reduced substantially without affecting ANC performance much. Finally, we expand the single-channel deep ANC to the multi-channel setup. The resulting approach, called deep MCANC, is developed for active noise control at multiple spatial points (multi-point ANC) and within a spatial zone (generating a quiet zone). In addition, we evaluate the performance of deep MCANC under different setups and examine the impact of factors such as the number of loudspeakers and microphones, and the position of a secondary source, on MCANC performance.

Author: Zhongwei Zhuang
Publisher:
ISBN:
Category : Noise control
Languages : en
Pages : 198

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Book Description

Author: Gillian M. Davis
Publisher: CRC Press
ISBN: 1351835998
Category : Technology & Engineering
Languages : en
Pages : 342

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Book Description
Noise and distortion that degrade the quality of speech signals can come from any number of sources. The technology and techniques for dealing with noise are almost as numerous, but it is only recently, with the development of inexpensive digital signal processing hardware, that the implementation of the technology has become practical. Noise Reduction in Speech Applications provides a comprehensive introduction to modern techniques for removing or reducing background noise from a range of speech-related applications. Self-contained, it starts with a tutorial-style chapter of background material, then focuses on system aspects, digital algorithms, and implementation. The final section explores a variety of applications and demonstrates to potential users of the technology the results possible with the noise reduction techniques presented. The book offers chapters contributed by international experts, a practical, systems approach, and numerous references. For electrical, acoustics, signal processing, communications, and bioengineers, Noise Reduction in Speech Applications is a valuable resource that shows you how to decide whether noise reduction will solve problems in your own systems and how to make the best use of the technologies available.

Author: Ivan Jelev Tashev
Publisher: John Wiley & Sons
ISBN: 9780470994436
Category : Technology & Engineering
Languages : en
Pages : 388

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Book Description
Provides state-of-the-art algorithms for sound capture, processing and enhancement Sound Capture and Processing: Practical Approaches covers the digital signal processing algorithms and devices for capturing sounds, mostly human speech. It explores the devices and technologies used to capture, enhance and process sound for the needs of communication and speech recognition in modern computers and communication devices. This book gives a comprehensive introduction to basic acoustics and microphones, with coverage of algorithms for noise reduction, acoustic echo cancellation, dereverberation and microphone arrays; charting the progress of such technologies from their evolution to present day standard. Sound Capture and Processing: Practical Approaches Brings together the state-of-the-art algorithms for sound capture, processing and enhancement in one easily accessible volume Provides invaluable implementation techniques required to process algorithms for real life applications and devices Covers a number of advanced sound processing techniques, such as multichannel acoustic echo cancellation, dereverberation and source separation Generously illustrated with figures and charts to demonstrate how sound capture and audio processing systems work An accompanying website containing Matlab code to illustrate the algorithms This invaluable guide will provide audio, R&D and software engineers in the industry of building systems or computer peripherals for speech enhancement with a comprehensive overview of the technologies, devices and algorithms required for modern computers and communication devices. Graduate students studying electrical engineering and computer science, and researchers in multimedia, cell-phones, interactive systems and acousticians will also benefit from this book.

Author: Colin N. Hansen
Publisher: CRC Press
ISBN: 0203467337
Category : Technology & Engineering
Languages : en
Pages : 173

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Book Description
Understanding Active Noise Cancellation Provides a concise introduction to the fundamentals and applications of active control of vibration and sound for the non-expert. It is also a useful quick reference for the specialist engineer. The book emphasises the practical applications of technology, and complex control algorithms and structures are only discussed to the extent that they aid understanding. Extensive recommendations for further reading on the subject are provided, but the text will stand alone for those seeking an overview of the key issues: fundamentals, control systems, transducers, applications and possible future directions.

Author: Jacob Benesty
Publisher: Springer Science & Business Media
ISBN: 3642225748
Category : Technology & Engineering
Languages : en
Pages : 141

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Book Description
Single-channel hands-free teleconferencing systems are becoming popular. In order to enhance the communication quality of these systems, more and more stereophonic sound devices with two loudspeakers and two microphones are deployed. Because of the coupling between loudspeakers and microphones, there may be strong echoes, which make real-time communication very difficult. The best way we know to cancel these echoes is via a stereo acoustic echo canceller (SAEC), which can be modelled as a two-input/two-output system with real random variables. In this work, the authors recast this problem into a single-input/single-output system with complex random variables thanks to the widely linear model. From this new convenient formulation, they re-derive the most important aspects of a SAEC, including identification of the echo paths with adaptive filters, double-talk detection, and suppression.