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)

Separating singing voice from music accompaniment has wide applications in areas such as automatic lyrics recognition and alignment, singer identification, and music information retrieval. Compared to the extensive studies of speech separation, singing voice separation has been little explored. We propose a system to separate singing voice from music accompaniment from monaural recordings. The system has three stages. The singing voice detection stage partitions and classifies an input into vocal and non-vocal portions. Then the predominant pitch detection stage detects the pitch contour of the singing voice for vocal portions. Finally the separation stage uses the detected pitch contour to group the time-frequency segments of the singing voice. Quantitative results show that the system performs well in singing voice separation.

/pdf/singing-voice-separation-from-monaural-recordings-tnxwa4qr34.pdf

Singing Voice Separation from Monaural Recordings.

It has been demonstrated that short-term habituation may be caused by a decrease in release of presynaptic neurotransmitters and long-term habituation seems to be caused by morphological changes of presynaptic terminals. A parsimonious model of short-term and long-term synaptic plasticity at the electrophysiological level is presented. This model consists of two interacting differential equations, one describing alterations of the synaptic weight and the other describing changes to the speed of recovery (forgetting). The latter exhibits an inverse S-shaped curve whose high value corresponds to fast recovery (short-term habituation) and low value corresponds to slow recovery (long-term habituation). The model has been tested on short-term and a set of long-term habituation data of prey-catching behavior in toads, spanning minutes to hours to several weeks.

/pdf/a-neural-model-of-synaptic-plasticity-underlying-short-term-1zus3s0ubv.pdf

A neural model of synaptic plasticity underlying short-term and long-term habituation

Despite successful applications of multi-channel signal processing in robust automatic speech recognition (ASR), relatively little research has been conducted on the effectiveness of such techniques in the robust speaker recognition domain. This paper introduces time-frequency (T-F) maskingbased beamforming to address text-independent speaker recognition in conditions where strong diffuse noise and reverberation are both present. We examine various masking-based beamformers, such as parameterized multi-channel Wiener filter, generalized eigenvalue (GEV) beamformer and minimum variance distortion-less response (MVDR) beamformer, and evaluate their performance in terms of speaker recognition accuracy for i-vector and x-vector based systems. In addition, we present a different formulation for estimating steering vectors from speech covariance matrices. We show that rank-1 approximation of a speech covariance matrix based on generalized eigenvalue decomposition leads to the best results for the masking-based MVDR beamformer. Experiments on the recently introduced NIST SRE 2010 retransmitted corpus show that the MVDR beamformer with rank-1 approximation provides an absolute reduction of 5.55% in equal error rate compared to a standard masking-based MVDR beamformer.

Deep Learning Based Multi-Channel Speaker Recognition in Noisy and Reverberant Environments.

This paper presents a novel method for tracking the azimuth locations of multiple active sources based on binaural processing Binaural cues are strongly correlated with source locations for spectral regions dominated by only one source Therefore, this approach integrates reliable information across different frequency channels to produce a likelihood function in the target space Finally, a hidden Markov model (HMM) is employed for forming continuous tracks and detecting the number of active sources across time Experimental results are presented for simulated multi-source scenarios

Binaural tracking of multiple moving sources

Existing binaural approaches to speech segregation place an exclusive burden on cues related to the location of sound sources in space. These approaches can achieve excellent performance in anechoic conditions but degrade rapidly in realistic environments where room reverberation corrupts localization cues. In this paper, we propose to integrate monaural and binaural processing to achieve segregation and localization of voiced speech in reverberant environments. The proposed approach builds on monaural analysis for simultaneous organization, and combines it with a novel method for generation of location-based cues in a probabilistic framework that jointly achieves localization and sequential organization. We compare localization performance to two existing methods, sequential organization performance to a model-based system that uses only monaural cues, and segregation performance to an exclusively binaural system. Results suggest that the proposed framework allows for improved source localization and robust segregation of voiced speech in environments with considerable reverberation.

/pdf/sequential-organization-of-speech-in-reverberant-149y69dnav.pdf

DeLiang Wang

Papers

Singing Voice Separation from Monaural Recordings.

A neural model of synaptic plasticity underlying short-term and long-term habituation

Deep Learning Based Multi-Channel Speaker Recognition in Noisy and Reverberant Environments.

Binaural tracking of multiple moving sources

Sequential Organization of Speech in Reverberant Environments by Integrating Monaural Grouping and Binaural Localization