Download or read book Speech Enhancement Exploiting the Source filter Model written by Samy Elshamy and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Imagining everyday life without mobile telephony is nowadays hardly possible. Calls are being made in every thinkable situation and environment. Hence, the microphone will not only pick up the user's speech but also sound from the surroundings which is likely to impede the understanding of the conversational partner. Modern speech enhancement systems are able to mitigate such effects and most users are not even aware of their existence. In this thesis the development of a modern single-channel speech enhancement approach is presented, which uses the divide and conquer principle to combat environmental noise in microphone signals. Though initially motivated by mobile telephony applications, this approach can be applied whenever speech is to be retrieved from a corrupted signal. The approach uses the so-called source-filter model to divide the problem into two subproblems which are then subsequently conquered by enhancing the source (the excitation signal) and the filter (the spectral envelope) separately. Both enhanced signals are then used to denoise the corrupted signal. The estimation of spectral envelopes has quite some history and some approaches already exist for speech enhancement. However, they typically neglect the excitation signal which leads to the inability of enhancing the fine structure properly. Both individual enhancement approaches exploit benefits of the cepstral domain which offers, e.g., advantageous mathematical properties and straightforward synthesis of excitation-like signals. We investigate traditional model-based schemes like Gaussian mixture models (GMMs), classical signal processing-based, as well as modern deep neural network (DNN)-based approaches in this thesis. The enhanced signals are not used directly to enhance the corrupted signal (e.g., to synthesize a clean speech signal) but as so-called a priori signal-to-noise ratio (SNR) estimate in a traditional statistical speech enhancement system. Such a traditional system consists of a noise power estimator, an a priori SNR estimator, and a spectral weighting rule that is usually driven by the results of the aforementioned estimators and subsequently employed to retrieve the clean speech estimate from the noisy observation. As a result the new approach obtains significantly higher noise attenuation compared to current state-of-the-art systems while maintaining a quite comparable speech component quality and speech intelligibility. In consequence, the overall quality of the enhanced speech signal turns out to be superior as compared to state-of-the-art speech ehnahcement approaches.

Download or read book Audio Source Separation and Speech Enhancement written by Emmanuel Vincent and published by John Wiley & Sons. This book was released on 2018-10-22 with total page 517 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Speech Enhancement written by Shoji Makino and published by Springer Science & Business Media. This book was released on 2005-03-17 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book A Perspective on Single Channel Frequency Domain Speech Enhancement written by Jacob Benesty and published by Morgan & Claypool Publishers. This book was released on 2011-03-01 with total page 111 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on a class of single-channel noise reduction methods that are performed in the frequency domain via the short-time Fourier transform (STFT). The simplicity and relative effectiveness of this class of approaches make them the dominant choice in practical systems. Even though many popular algorithms have been proposed through more than four decades of continuous research, there are a number of critical areas where our understanding and capabilities still remain quite rudimentary, especially with respect to the relationship between noise reduction and speech distortion. All existing frequency-domain algorithms, no matter how they are developed, have one feature in common: the solution is eventually expressed as a gain function applied to the STFT of the noisy signal only in the current frame. As a result, the narrowband signal-to-noise ratio (SNR) cannot be improved, and any gains achieved in noise reduction on the fullband basis come with a price to pay, which is speech distortion. In this book, we present a new perspective on the problem by exploiting the difference between speech and typical noise in circularity and interframe self-correlation, which were ignored in the past. By gathering the STFT of the microphone signal of the current frame, its complex conjugate, and the STFTs in the previous frames, we construct several new, multiple-observation signal models similar to a microphone array system: there are multiple noisy speech observations, and their speech components are correlated but not completely coherent while their noise components are presumably uncorrelated. Therefore, the multichannel Wiener filter and the minimum variance distortionless response (MVDR) filter that were usually associated with microphone arrays will be developed for single-channel noise reduction in this book. This might instigate a paradigm shift geared toward speech distortionless noise reduction techniques.

Download or read book New Era for Robust Speech Recognition written by Shinji Watanabe and published by Springer. This book was released on 2017-10-30 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the state-of-the-art in deep neural-network-based methods for noise robustness in distant speech recognition applications. It provides insights and detailed descriptions of some of the new concepts and key technologies in the field, including novel architectures for speech enhancement, microphone arrays, robust features, acoustic model adaptation, training data augmentation, and training criteria. The contributed chapters also include descriptions of real-world applications, benchmark tools and datasets widely used in the field. This book is intended for researchers and practitioners working in the field of speech processing and recognition who are interested in the latest deep learning techniques for noise robustness. It will also be of interest to graduate students in electrical engineering or computer science, who will find it a useful guide to this field of research.

Download or read book Model based Speech Enhancement Exploiting Temporal and Spectral Dependencies written by Thomas Esch and published by . This book was released on 2012 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Speech Enhancement in the STFT Domain written by Jacob Benesty and published by Springer Science & Business Media. This book was released on 2011-09-18 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work addresses this problem in the short-time Fourier transform (STFT) domain. We divide the general problem into five basic categories depending on the number of microphones being used and whether the interframe or interband correlation is considered. The first category deals with the single-channel problem where STFT coefficients at different frames and frequency bands are assumed to be independent. In this case, the noise reduction filter in each frequency band is basically a real gain. Since a gain does not improve the signal-to-noise ratio (SNR) for any given subband and frame, the noise reduction is basically achieved by liftering the subbands and frames that are less noisy while weighing down on those that are more noisy. The second category also concerns the single-channel problem. The difference is that now the interframe correlation is taken into account and a filter is applied in each subband instead of just a gain. The advantage of using the interframe correlation is that we can improve not only the long-time fullband SNR, but the frame-wise subband SNR as well. The third and fourth classes discuss the problem of multichannel noise reduction in the STFT domain with and without interframe correlation, respectively. In the last category, we consider the interband correlation in the design of the noise reduction filters. We illustrate the basic principle for the single-channel case as an example, while this concept can be generalized to other scenarios. In all categories, we propose different optimization cost functions from which we derive the optimal filters and we also define the performance measures that help analyzing them.

Download or read book Audio Source Separation and Speech Enhancement written by Emmanuel Vincent and published by John Wiley & Sons. This book was released on 2018-07-24 with total page 506 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Speech Enhancement written by Philipos C. Loizou and published by CRC Press. This book was released on 2007-06-07 with total page 640 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers traditional speech enhancement algorithms, such as spectral subtraction and Wiener filtering algorithms as well as state-of-the-art algorithms including minimum mean-squared error algorithms that incorporate signal-presence uncertainty and subspace algorithms that incorporate psychoacoustic models. The coverage includes objective and subjective measures used to evaluate speech quality and intelligibility. Divided into three parts, the book presents the digital-signal processing and speech signal fundamentals needed to understand speech enhancement algorithms, the various classes of speech enhancement algorithms proposed over the last two decades, and the methods and measures used to evaluate the performance of speech enhancement algorithms.

Download or read book Distant Speech Recognition written by Matthias Woelfel and published by John Wiley & Sons. This book was released on 2009-04-20 with total page 600 pages. Available in PDF, EPUB and Kindle. Book excerpt: A complete overview of distant automatic speech recognition The performance of conventional Automatic Speech Recognition (ASR) systems degrades dramatically as soon as the microphone is moved away from the mouth of the speaker. This is due to a broad variety of effects such as background noise, overlapping speech from other speakers, and reverberation. While traditional ASR systems underperform for speech captured with far-field sensors, there are a number of novel techniques within the recognition system as well as techniques developed in other areas of signal processing that can mitigate the deleterious effects of noise and reverberation, as well as separating speech from overlapping speakers. Distant Speech Recognitionpresents a contemporary and comprehensive description of both theoretic abstraction and practical issues inherent in the distant ASR problem. Key Features: Covers the entire topic of distant ASR and offers practical solutions to overcome the problems related to it Provides documentation and sample scripts to enable readers to construct state-of-the-art distant speech recognition systems Gives relevant background information in acoustics and filter techniques, Explains the extraction and enhancement of classification relevant speech features Describes maximum likelihood as well as discriminative parameter estimation, and maximum likelihood normalization techniques Discusses the use of multi-microphone configurations for speaker tracking and channel combination Presents several applications of the methods and technologies described in this book Accompanying website with open source software and tools to construct state-of-the-art distant speech recognition systems This reference will be an invaluable resource for researchers, developers, engineers and other professionals, as well as advanced students in speech technology, signal processing, acoustics, statistics and artificial intelligence fields.

Download or read book Cognitively Inspired Audiovisual Speech Filtering written by Andrew Abel and published by Springer. This book was released on 2015-08-07 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a summary of the cognitively inspired basis behind multimodal speech enhancement, covering the relationship between audio and visual modalities in speech, as well as recent research into audiovisual speech correlation. A number of audiovisual speech filtering approaches that make use of this relationship are also discussed. A novel multimodal speech enhancement system, making use of both visual and audio information to filter speech, is presented, and this book explores the extension of this system with the use of fuzzy logic to demonstrate an initial implementation of an autonomous, adaptive, and context aware multimodal system. This work also discusses the challenges presented with regard to testing such a system, the limitations with many current audiovisual speech corpora, and discusses a suitable approach towards development of a corpus designed to test this novel, cognitively inspired, speech filtering system.

Download or read book Fundamentals of Speech Enhancement written by Jacob Benesty and published by Springer. This book was released on 2018-02-09 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents and develops several important concepts of speech enhancement in a simple but rigorous way. Many of the ideas are new; not only do they shed light on this old problem but they also offer valuable tips on how to improve on some well-known conventional approaches. The book unifies all aspects of speech enhancement, from single channel, multichannel, beamforming, time domain, frequency domain and time–frequency domain, to binaural in a clear and flexible framework. It starts with an exhaustive discussion on the fundamental best (linear and nonlinear) estimators, showing how they are connected to various important measures such as the coefficient of determination, the correlation coefficient, the conditional correlation coefficient, and the signal-to-noise ratio (SNR). It then goes on to show how to exploit these measures in order to derive all kinds of noise reduction algorithms that can offer an accurate and versatile compromise between noise reduction and speech distortion.

Download or read book Audio Source Separation written by Shoji Makino and published by Springer. This book was released on 2018-03-01 with total page 389 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Speech Enhancement written by Jacob Benesty and published by Elsevier. This book was released on 2014-01-04 with total page 143 pages. Available in PDF, EPUB and Kindle. Book excerpt: Speech enhancement is a classical problem in signal processing, yet still largely unsolved. Two of the conventional approaches for solving this problem are linear filtering, like the classical Wiener filter, and subspace methods. These approaches have traditionally been treated as different classes of methods and have been introduced in somewhat different contexts. Linear filtering methods originate in stochastic processes, while subspace methods have largely been based on developments in numerical linear algebra and matrix approximation theory. This book bridges the gap between these two classes of methods by showing how the ideas behind subspace methods can be incorporated into traditional linear filtering. In the context of subspace methods, the enhancement problem can then be seen as a classical linear filter design problem. This means that various solutions can more easily be compared and their performance bounded and assessed in terms of noise reduction and speech distortion. The book shows how various filter designs can be obtained in this framework, including the maximum SNR, Wiener, LCMV, and MVDR filters, and how these can be applied in various contexts, like in single-channel and multichannel speech enhancement, and in both the time and frequency domains. - First short book treating subspace approaches in a unified way for time and frequency domains, single-channel, multichannel, as well as binaural, speech enhancement - Bridges the gap between optimal filtering methods and subspace approaches - Includes original presentation of subspace methods from different perspectives

Download or read book Speech Enhancement Using Training based Non negative Matrix Factorization Techniques written by Hanwook Chung and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "In this thesis, we develop novel training-based non-negative matrix factorization (NMF) algorithms for single and multi-channel speech enhancement.After introducing the problem and reviewing background material, we first present a regularized NMF algorithm with Gaussian mixtures and masking model for single-channel speech enhancement. The proposed framework seeks to exploit the statistical properties of the clean speech and noise. This is accomplished by including the log-likelihood functions (LLF) of the clean speech and noise magnitude spectra, based on Gaussian mixture models (GMM), as the regularization terms in the NMF cost function. Moreover, we incorporate the masking effects of the human auditory system to further improve the enhanced speech quality.Second, we introduce a training and compensation algorithm of the class-conditioned NMF model for single-channel speech enhancement. The main goal is to reduce the residual noise components that have features similar to the speech. To this end, during the training stage, the basis vectors of different sources are obtained in a way that prevents them from representing each other, based on the concept of classification. Another goal is to handle the mismatch between the characteristics of the training and test data. This is accomplished by employing extra free basis vectors during the enhancement stage to capture the features which are not included in the training data.Finally, we present a novel multi-channel speech enhancement algorithm based on a Bayesian NMF model. Essentially, we consider the Poisson-distributed latent variable model for multi-channel NMF. During the training stage, the NMF parameters are estimated from the tensor-based training data. During the enhancement stage, the clean speech signal is estimated via the NMF-based minimum variance distortionless response (MVDR) beamforming technique. To this end, the source locations are estimated by observing the spatial output power of a delay-and-sum (DS) beamformer applied to the NMF-based pre-processed noisy speech signal.For each one of the above algorithms, objective experiments are carried out for different combinations of speaker, noise types and signal-to-noise ratio. The results show that the proposed methods provide better speech enhancement performance than the selected benchmark algorithms under considered test conditions." --

Download or read book Robust Automatic Speech Recognition written by Jinyu Li and published by Academic Press. This book was released on 2015-10-30 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book DFT Domain Based Single Microphone Noise Reduction for Speech Enhancement written by Richard C. Hendriks and published by Morgan & Claypool Publishers. This book was released on 2013-01-01 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt: As speech processing devices like mobile phones, voice controlled devices, and hearing aids have increased in popularity, people expect them to work anywhere and at any time without user intervention. However, the presence of acoustical disturbances limits the use of these applications, degrades their performance, or causes the user difficulties in understanding the conversation or appreciating the device. A common way to reduce the effects of such disturbances is through the use of single-microphone noise reduction algorithms for speech enhancement. The field of single-microphone noise reduction for speech enhancement comprises a history of more than 30 years of research. In this survey, we wish to demonstrate the significant advances that have been made during the last decade in the field of discrete Fourier transform domain-based single-channel noise reduction for speech enhancement.Furthermore, our goal is to provide a concise description of a state-of-the-art speech enhancement system, and demonstrate the relative importance of the various building blocks of such a system. This allows the non-expert DSP practitioner to judge the relevance of each building block and to implement a close-to-optimal enhancement system for the particular application at hand. Table of Contents: Introduction / Single Channel Speech Enhancement: General Principles / DFT-Based Speech Enhancement Methods: Signal Model and Notation / Speech DFT Estimators / Speech Presence Probability Estimation / Noise PSD Estimation / Speech PSD Estimation / Performance Evaluation Methods / Simulation Experiments with Single-Channel Enhancement Systems / Future Directions