Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Deep Learning

http://www.maths.qmul.ac.uk/%7Elatora/report_06.pdf

Complex networks: Structure and dynamics

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science. Neural Networks and Learning Machines, Third Edition is renowned for its thoroughness and readability. This well-organized and completely upto-date text remains the most comprehensive treatment of neural networks from an engineering perspective. This is ideal for professional engineers and research scientists. Matlab codes used for the computer experiments in the text are available for download at: http://www.pearsonhighered.com/haykin/ Refocused, revised and renamed to reflect the duality of neural networks and learning machines, this edition recognizes that the subject matter is richer when these topics are studied together. Ideas drawn from neural networks and machine learning are hybridized to perform improved learning tasks beyond the capability of either independently.

Neural Networks And Learning Machines

1980 Preface * 1999 Preface * 1999 Acknowledgements * Introduction * 1 Circular Logic * 2 Phase Singularities (Screwy Results of Circular Logic) * 3 The Rules of the Ring * 4 Ring Populations * 5 Getting Off the Ring * 6 Attracting Cycles and Isochrons * 7 Measuring the Trajectories of a Circadian Clock * 8 Populations of Attractor Cycle Oscillators * 9 Excitable Kinetics and Excitable Media * 10 The Varieties of Phaseless Experience: In Which the Geometrical Orderliness of Rhythmic Organization Breaks Down in Diverse Ways * 11 The Firefly Machine 12 Energy Metabolism in Cells * 13 The Malonic Acid Reagent ('Sodium Geometrate') * 14 Electrical Rhythmicity and Excitability in Cell Membranes * 15 The Aggregation of Slime Mold Amoebae * 16 Numerical Organizing Centers * 17 Electrical Singular Filaments in the Heart Wall * 18 Pattern Formation in the Fungi * 19 Circadian Rhythms in General * 20 The Circadian Clocks of Insect Eclosion * 21 The Flower of Kalanchoe * 22 The Cell Mitotic Cycle * 23 The Female Cycle * References * Index of Names * Index of Subjects

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/596B270197FE27DE5FABB598B6210622/S0025557200150892a.pdf/div-class-title-span-class-italic-the-geometry-of-biological-time-span-by-a-t-winfree-pp-544-dm68-corrected-second-printing-1990-isbn-3-540-52528-9-springer-div.pdf

The geometry of biological time , by A. T. Winfree. Pp 544. DM68. Corrected Second Printing 1990. ISBN 3-540-52528-9 (Springer)

In this work, we propose a new model called triple-path attentive recurrent network (TPARN) for multichannel speech enhancement in the time domain. TPARN extends a single-channel dual-path network to a multichannel network by adding a third path along the spatial dimension. First, TPARN processes speech signals from all channels independently using a dual-path attentive recurrent network (ARN), which is a recurrent neural network (RNN) augmented with self-attention. Next, an ARN is introduced along the spatial dimension for spatial context aggregation. TPARN is designed as a multiple-input and multiple-output architecture to enhance all input channels simultaneously. Experimental results demonstrate the superiority of TPARN over existing state-of-the-art approaches.

TPARN: Triple-path Attentive Recurrent Network for Time-domain Multichannel Speech Enhancement.

Localization of simultaneous sound sources in natural environments with only two microphones is a challenging problem. Reverberation degrades performance of localization based exclusively on directional cues. We present an approach that integrates monaural and binaural analysis to improve localization of multiple speech sources in noisy and reverberant environments. Our approach incorporates pitch-based monaural processing to perform simultaneous organization of voiced speech. We propose a probabilistic framework to jointly perform localization and sequential organization using binaural cues. We evaluate our system on multi-source speech mixtures in the presence of reverberation and diffuse noise and compare it to two localization approaches that do not incorporate monaural cues. Results indicate that our system can accurately localize multiple sources in very challenging conditions.

Integrating monaural and binaural analysis for localizing multiple reverberant sound sources

Unvoiced speech poses a big challenge to current monaural speech segregation systems. It lacks harmonic structure and is highly susceptible to interference due to its relatively weak energy. This paper describes a new approach to segregate unvoiced speech from nonspeech interference. The system first estimates a voiced binary mask, and then performs unvoiced speech segregation in two stages: segmentation and grouping. In segmentation, time-frequency units labeled as 0 in the voiced binary mask are first used to estimate the noise energy and spectral subtraction is then performed to generate time-frequency segments in unvoiced intervals. Based on the type of noise, unvoiced segments are grouped either by selecting segments consistent with those generated by onset/offset analysis or by Bayesian classification of acoustic-phonetic features. Systematic evaluation and comparison show that the proposed approach improves the performance of unvoiced speech segregation considerably.

Incorporating spectral subtraction and noise type for unvoiced speech segregation

Acoustic energy from many different sources is present in the environment at all times. In order for a listener to recognize and understand the auditory environment, it is necessary to disentangle the acoustic wave form and analyze each separate event. This process is referred to as temporal pattern segmentation, or auditory scene analysis (Bregman, 1990). Its task is to break down an auditory scene, or total acoustic input, into a number of coherent segments, each of which has a high probability of coming from the same source. Temporal pattern segmentation (temporal segmentation for short) is a remarkable achievement of the auditory system, playing a fundamental role in auditory perception. It has much in common with visual segmentation of a scene into different objects. Segmentation can be based on either current input or prior knowledge. Segmentation based on current input relies on the similarities of local qualities within the input pattern itself, such as frequency, timing, or amplitude. On the other hand, segmentation based on prior knowledge relies on patterns stored in memory to segregate the auditory input. These two processes occur simultaneously in auditory scene analysis.

An Oscillatory Correlation Theory of Temporal Pattern Segmentation

Most existing binaural approaches to speech segregation rely on spatial filtering In environments with minimal reverberation and when sources are well separated in space, spatial filtering can achieve excellent results However, in everyday environments performance degrades substantially To address these limitations, we incorporate monaural analysis within a binaural segregation system We use monaural cues to perform both local and across frequency grouping of mixture components, allowing for a more robust application of spatial filtering We propose a novel framework in which we combine monaural grouping evidence and binaural localization evidence in a linear model for the estimation of the ideal binary mask Results indicate that with appropriately designed features that capture both monaural and binaural evidence, an extremely simple model achieves a signal-to-noise ratio improvement of up to 36 dB relative to using spatial filtering alone Index Terms: Speech segregation, binaural localization, monaural grouping, linear model

DeLiang Wang

Papers

TPARN: Triple-path Attentive Recurrent Network for Time-domain Multichannel Speech Enhancement.

Integrating monaural and binaural analysis for localizing multiple reverberant sound sources

Incorporating spectral subtraction and noise type for unvoiced speech segregation

An Oscillatory Correlation Theory of Temporal Pattern Segmentation

Combining Monaural and Binaural Evidence for Reverberant Speech Segregation