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Author: Bracha Laufer-Goldshtein Publisher: ISBN: 9781680837360 Category : Languages : en Pages : 178
Book Description
Acoustic source localization is an essential component in many modern day audio applications. For example, smart speakers require localization capabilities in order to determine the speakers in the scene and their role. Based on the location information, they can enhance a speaker or carry out location specific tasks, such as switching the lights on and off, steering a camera, etc. Localization has often been based on creating physical models which become extremely intricate in real-world applications. Recently, researchers have started using learning techniques to address localization problems. This monograph introduces the reader to the research and practical aspects behind the approach of learning the characteristics of the acoustic environment directly from the data rather than using a predefined physical model. Written by the experts in the field who have developed many of these techniques, it provides a comprehensive overview and insights into this burgeoning area of acoustic developments. The reader is introduced to the underlying mathematics before being introduced to the localization problem in depth. The core paradigm of using manifolds for diffusion mapping and distance is then described. Building on these concepts, the authors address both single and multiple manifold localization. Finally, manifold-based tracking is covered. Data-Driven Multi-Microphone Speaker Localization on Manifolds is an illuminating introduction to designing and building acoustic systems where localization of multi-microphone and speakers forms an essential part of the system.
Author: Bracha Laufer-Goldshtein Publisher: ISBN: 9781680837360 Category : Languages : en Pages : 178
Book Description
Acoustic source localization is an essential component in many modern day audio applications. For example, smart speakers require localization capabilities in order to determine the speakers in the scene and their role. Based on the location information, they can enhance a speaker or carry out location specific tasks, such as switching the lights on and off, steering a camera, etc. Localization has often been based on creating physical models which become extremely intricate in real-world applications. Recently, researchers have started using learning techniques to address localization problems. This monograph introduces the reader to the research and practical aspects behind the approach of learning the characteristics of the acoustic environment directly from the data rather than using a predefined physical model. Written by the experts in the field who have developed many of these techniques, it provides a comprehensive overview and insights into this burgeoning area of acoustic developments. The reader is introduced to the underlying mathematics before being introduced to the localization problem in depth. The core paradigm of using manifolds for diffusion mapping and distance is then described. Building on these concepts, the authors address both single and multiple manifold localization. Finally, manifold-based tracking is covered. Data-Driven Multi-Microphone Speaker Localization on Manifolds is an illuminating introduction to designing and building acoustic systems where localization of multi-microphone and speakers forms an essential part of the system.
Author: Bracha Laufter-Goldshtein Publisher: ISBN: 9781680837377 Category : Acoustic localization Languages : en Pages : 161
Book Description
Speech enhancement is a core problem in audio signal processing with commercial applications in devices as diverse as mobile phones, conference call systems, smart assistants, and hearing aids. An essential component in the design of speech enhancement algorithms is acoustic source localization. Speaker localization is also directly applicable to many other audio related tasks, e.g., automated camera steering, teleconferencing systems, and robot audition. From a signal processing perspective, speaker localization is the task of mapping multichannel speech signals to 3-D source coordinates. To obtain viable solutions for this mapping, an accurate description of the source wave propagation captured by the respective acoustic channel is required. In fact, the acoustic channels can be considered as the spatial fingerprints characterizing the positions of each of the sources in a reverberant enclosure. These fingerprints represent complex reflection patterns stemming from the surfaces and objects characterizing the enclosure. Hence, they are usually modelled by a very large number of coefficients, resulting in an intricate high-dimensional representation. We claim that in static acoustic environments, despite the high dimensional representation, the difference between acoustic channels can be attributed mainly to changes in the source position. Thus, the true intrinsic dimensionality of the variations of the acoustic channels are significantly smaller than the number of variables commonly used to represent them; that is, the acoustic channels pertain to a low-dimensional manifold that can be inferred from data using nonlinear dimensionality reduction techniques. A comprehensive experimental study carried out in a real-life acoustic environment demonstrates the validity of the proposed manifold-based paradigm. Motivated by this result, several high-performance localization and tracking methods were developed by harnessing novel mathematical tools for learning over manifolds, including diffusion maps, semi-supervised learning, optimization in reproducing kernel Hilbert spaces and Gaussian process inference. We present two localization algorithms that were designed for a single microphone array of two microphones. These algorithms were extended to several distributed arrays by merging the information of the different manifolds associated with each array. Tracking a moving source was also addressed by a data-driven propagation model relating movements on the abstract manifold to the actual source displacements. This data-driven propagation model was combined with a classical localization approach, in a hybrid algorithm that ties together the two worlds of classical and data-driven localization, while gaining the benefits of both. We show that the proposed algorithms outperform state-of-the-art localization methods, and obtain high accuracy in challenging noisy and reverberant environments.
Author: Yifan Wu Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
Wideband source localization is an important problem in signal processing, and it has wide-range applications in underwater acoustics, indoor speaker localization, teleconferencing, and etc. Over the past few decades, there are significant amount of methods proposed for the wideband source localization. However, it still remains a challenging problem. This dissertation tackles the wideband source localization from data-driven and model-based perspectives. For the data-driven part, a novel deep learning framework for the sound source localization (SSL) was proposed. SSL is to estimate the locations of the sound sources based on the received signal from the microphone array. SSL in the reverberant environment can be challenging due to the multipath artifacts in the received signals. To tackle with this challenge, a deep learning framework based on multi-task learning and image translation (MTIT) network is proposed. MTIT utilizes the encoder-decoder structure and it consists of one encoder and two decoders. The encoder aims to obtain a compressed representation of the input while the two decoders focus on two tasks in parallel. One decoder focuses on mitigating the multipath caused by reverberation and the other decoder predicts the source location. Due to the explicit dereverberation module and the shared encoder (representation), the proposed localization framework can achieve superior performance and can generalize to the unseen data in the reverberant environment compared to the existing baseline methods. For the model-based part, gridless direction-of-arrival (DOA) estimation based on atomic norm minimization (ANM) for the multi-frequency signal was studied. ANM was formulated to an equivalent computationally feasible semi-definite program (SDP) problem. The dual certificate condition is given to certify the optimality. A fast algorithm implementation is given and the dual problem of the SDP is considered. The method is further generalized to the non-uniform array and non-uniform frequency case. Extensive theoretical analysis and numerical experiments demonstrate the superior performance of the proposed method compared to sparse Bayesian learning, the existing grid-based multi-frequency DOA estimation method.
Author: Evan Ettinger Publisher: ISBN: Category : Computer algorithms Languages : en Pages : 5
Book Description
We present an algorithm for high dimensional density estimation which is efficient (both computationally and statistically) when the distribution is concentrated close to a low dimensional smooth manifold. The algorithm uses several random projections to generate a hierarchical mixture of Gaussians which rapidly converges to the underlying manifold. We use this algorithm to perform robust estimation of the time delays in an ad-hoc microphone network. We utilize the model to calculate accurate time-delay vectors for two speakers that are talking at the same time.
Author: Shoji Makino Publisher: Springer ISBN: 3319730312 Category : Technology & Engineering Languages : en Pages : 389
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: Patrick A. Naylor Publisher: Springer Science & Business Media ISBN: 1849960569 Category : Technology & Engineering Languages : en Pages : 388
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: Francesco Camastra Publisher: Springer ISBN: 144716735X Category : Computers Languages : en Pages : 564
Book Description
This second edition focuses on audio, image and video data, the three main types of input that machines deal with when interacting with the real world. A set of appendices provides the reader with self-contained introductions to the mathematical background necessary to read the book. Divided into three main parts, From Perception to Computation introduces methodologies aimed at representing the data in forms suitable for computer processing, especially when it comes to audio and images. Whilst the second part, Machine Learning includes an extensive overview of statistical techniques aimed at addressing three main problems, namely classification (automatically assigning a data sample to one of the classes belonging to a predefined set), clustering (automatically grouping data samples according to the similarity of their properties) and sequence analysis (automatically mapping a sequence of observations into a sequence of human-understandable symbols). The third part Applications shows how the abstract problems defined in the second part underlie technologies capable to perform complex tasks such as the recognition of hand gestures or the transcription of handwritten data. Machine Learning for Audio, Image and Video Analysis is suitable for students to acquire a solid background in machine learning as well as for practitioners to deepen their knowledge of the state-of-the-art. All application chapters are based on publicly available data and free software packages, thus allowing readers to replicate the experiments.
Author: Michael Robinson Publisher: Springer Science & Business Media ISBN: 3642361048 Category : Technology & Engineering Languages : en Pages : 245
Book Description
Signal processing is the discipline of extracting information from collections of measurements. To be effective, the measurements must be organized and then filtered, detected, or transformed to expose the desired information. Distortions caused by uncertainty, noise, and clutter degrade the performance of practical signal processing systems. In aggressively uncertain situations, the full truth about an underlying signal cannot be known. This book develops the theory and practice of signal processing systems for these situations that extract useful, qualitative information using the mathematics of topology -- the study of spaces under continuous transformations. Since the collection of continuous transformations is large and varied, tools which are topologically-motivated are automatically insensitive to substantial distortion. The target audience comprises practitioners as well as researchers, but the book may also be beneficial for graduate students.
Author: Sebastian Thrun Publisher: MIT Press ISBN: 9780262201520 Category : Models, Neurological Languages : en Pages : 1694
Book Description
Papers presented at the 2003 Neural Information Processing Conference by leading physicists, neuroscientists, mathematicians, statisticians, and computer scientists. The annual Neural Information Processing (NIPS) conference is the flagship meeting on neural computation. It draws a diverse group of attendees -- physicists, neuroscientists, mathematicians, statisticians, and computer scientists. The presentations are interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, brain imaging, vision, speech and signal processing, reinforcement learning and control, emerging technologies, and applications. Only thirty percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. This volume contains all the papers presented at the 2003 conference.
Author: Bracha Laufer-Goldshtein
Author: Bracha Laufer-Goldshtein
Author: Bracha Laufter-Goldshtein
Author: Yifan Wu
Author: Ofer Schwartz
Author: Evan Ettinger
Author: Shoji Makino
Author: Patrick A. Naylor
Author: Francesco Camastra
Author: Michael Robinson
Author: Sebastian Thrun