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| Author | : Niall Cahill |
| Publisher | : |
| Total Pages | : 194 |
| Release | : 2006 |
| Genre | : Electronic Engineering Theses |
| ISBN | : |
| Author | : Emmanuel Vincent |
| Publisher | : John Wiley & Sons |
| Total Pages | : 628 |
| Release | : 2018-07-24 |
| Genre | : Technology & Engineering |
| ISBN | : 1119279917 |
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 | : Peter Vary |
| Publisher | : John Wiley & Sons |
| Total Pages | : 596 |
| Release | : 2023-11-23 |
| Genre | : Technology & Engineering |
| ISBN | : 1119060982 |
DIGITAL SPEECH TRANSMISSION AND ENHANCEMENT Enables readers to understand the latest developments in speech enhancement/transmission due to advances in computational power and device miniaturization The Second Edition of Digital Speech Transmission and Enhancement has been updated throughout to provide all the necessary details on the latest advances in the theory and practice in speech signal processing and its applications, including many new research results, standards, algorithms, and developments which have recently appeared and are on their way into state-of-the-art applications. Besides mobile communications, which constituted the main application domain of the first edition, speech enhancement for hearing instruments and man-machine interfaces has gained significantly more prominence in the past decade, and as such receives greater focus in this updated and expanded second edition. Readers can expect to find information and novel methods on: Low-latency spectral analysis-synthesis, single-channel and dual-channel algorithms for noise reduction and dereverberation Multi-microphone processing methods, which are now widely used in applications such as mobile phones, hearing aids, and man-computer interfaces Algorithms for near-end listening enhancement, which provide a significantly increased speech intelligibility for users at the noisy receiving side of their mobile phone Fundamentals of speech signal processing, estimation and machine learning, speech coding, error concealment by soft decoding, and artificial bandwidth extension of speech signals Digital Speech Transmission and Enhancement is a single-source, comprehensive guide to the fundamental issues, algorithms, standards, and trends in speech signal processing and speech communication technology, and as such is an invaluable resource for engineers, researchers, academics, and graduate students in the areas of communications, electrical engineering, and information technology.
| 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.
| 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.
| Author | : Jiucang Hao |
| Publisher | : |
| Total Pages | : 117 |
| Release | : 2008 |
| Genre | : |
| ISBN | : |
Statistical signal processing has been very successful. We proposed novel probabilistic models and developed efficient algorithms for two important problems: speech enhancement and source separation. Part I focused on the speech enhancement. We developed two models with efficient algorithms. The first one assumed a Gaussian Mixture Model (GMM) in the log-spectral domain for speech prior which was trained by expectation maximization (EM) algorithm. Three approximations were employed to enhance the computational efficiency. The Laplace method estimated the signal by computing the mode of the posterior distribution, either in the frequency domain or in the log-spectrum domain. The Gaussian approximation converted the GMM in the log-spectrum domain into a GMM in the frequency domain by minimizing the KL-divergence. It provided an efficient gain and noise spectrum estimation with the EM algorithm. The second one used a Gaussian scale mixture model (GSMM) as speech prior. This model specified a stochastic dependency between the log-spectra and the frequency components which can be estimated simultaneously with GSMM. The algorithms for training the model and signal estimation were developed. All these algorithms were evaluated by applying them to enhance the speeches corrupted by the speech shaped noise (SSN). The experimental results demonstrated that the proposed algorithms improved the signal-to-noise ratio and lowered the word recognition error rate. In part II, a novel probabilistic framework based on Independent Vector Analysis (IVA) was proposed to separate the convolutive mixture of sources. IVA assumed a multidimensional GMM for the source priors. The joint modeling of all frequency bins originating from the same source prevented the permutation disorder that associated with independent component analysis (ICA). The GMM source priors could adapt to the statistics of the sources and enable IVA to separate different type of signals. We developed EM algorithms for both the noiseless case and noisy case. For noiseless case, an online algorithm was developed to handle non-stationary environments. For noisy case, noise reduction was achieved together with the separation processes. The algorithms were evaluated by applying them to separate the mixtures of speech and music. The experimental results showed improved performance over other algorithms.
| Author | : Israel Cohen |
| Publisher | : Springer |
| Total Pages | : 342 |
| Release | : 2010-02-04 |
| Genre | : Technology & Engineering |
| ISBN | : 9783642111297 |
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.
| Author | : Jacob Benesty |
| Publisher | : Elsevier |
| Total Pages | : 143 |
| Release | : 2014-01-04 |
| Genre | : Technology & Engineering |
| ISBN | : 0128002530 |
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
| Author | : Sagar Shah |
| Publisher | : |
| Total Pages | : 91 |
| Release | : 2019 |
| Genre | : Biomedical engineering |
| ISBN | : 9781088327920 |
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 | : Pierre Divenyi |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 328 |
| Release | : 2006-01-16 |
| Genre | : Technology & Engineering |
| ISBN | : 0387227946 |
This book is appropriate for those specializing in speech science, hearing science, neuroscience, or computer science and engineers working on applications such as automatic speech recognition, cochlear implants, hands-free telephones, sound recording, multimedia indexing and retrieval.
