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)

We propose a robust algorithm to detect the pitch of a singing voice in polyphonic audio. A new channel/peak selection scheme is introduced to exploit the salience of the singing voice and the beating phenomenon in high frequency channels. An HMM is employed to integrate the periodicity information across frequency channels and time frames. Quantitative evaluation shows that the new system performs significantly better than existing algorithms for predominant pitch detection in polyphonic audio.

/pdf/detecting-pitch-of-singing-voice-in-polyphonic-audio-54jgin8xwe.pdf

Detecting pitch of singing voice in polyphonic audio

We present a system for robust signal-to-noise ratio (SNR) estimation based on computational auditory scene analysis (CASA) The proposed algorithm uses an estimate of the ideal binary mask to segregate a time-frequency representation of the noisy signal into speech dominated and noise dominated regions Energy within each of these regions is summated to derive the filtered global SNR An SNR transform is introduced to convert the estimated filtered SNR to the true broadband SNR of the noisy signal The algorithm is further extended to estimate subband SNRs Evaluations are done using the TIMIT speech corpus and the NOISEX92 noise database Results indicate that both global and subband SNR estimates are superior to those of existing methods, especially at low SNR conditions

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A CASA-Based System for Long-Term SNR Estimation

Recent research shows that the i-vector framework for speaker recognition can significantly benefit from phonetic information. A common approach is to use a deep neural network (DNN) trained for automatic speech recognition to generate a universal background model (UBM). Studies in this area have been done in relatively clean conditions. However, strong background noise is known to severely reduce speaker recognition performance. This study investigates a phonetically-aware i-vector system in noisy conditions. We propose a front-end to tackle the noise problem by performing speech separation and examine its performance for both verification and identification tasks. The proposed separation system trains a DNN to estimate the ideal ratio mask of the noisy speech. The separated speech is then used to extract enhanced features for the i-vector framework. We compare the proposed system against a multi-condition trained baseline and a traditional GMM-UBM i-vector system. Our proposed system provides an absolute average improvement of 8% in identification accuracy and 1.2% in equal error rate.

Robust speaker recognition based on DNN/i-vectors and speech separation

Human speech is usually distorted by room reverberation These corruptions degrade speech quality and intelligibility, especially under a long reverberation time, and they also pose a serious problem for many speech-related applications such as automatic speech recognition In this paper, we propose a supervised speech dereverberation algorithm that models late reverberation using a recurrent neural network (RNN) with long short-term memory (LSTM) By taking advantage of LSTM's ability to capture a long history, late reverberation can be effectively removed by the proposed approach Systematic evaluations indicate that our approach improves the quality of reverberant speech in a wide range of reverberant conditions Moreover, the proposed system is a causal system, which can be applied in real-time applications

Late Reverberation Suppression Using Recurrent Neural Networks with Long Short-Term Memory

This paper tightly integrates spectral and spatial information for deep learning based multi-channel speaker separation. The key idea is to localize individual speakers so that an enhancement network can be used to separate the speaker from an estimated direction and with specific spectral characteristics. To determine the direction of the speaker of interest, we identify time-frequency (T-F) units dominated by that speaker and only use them for direction of arrival (DOA) estimation. The speaker dominance at each T-F unit is determined by a twochannel permutation invariant training network, which combines spectral and interchannel phase patterns at the input feature level. In addition, beamforming is tightly integrated in the proposed system by exploiting the magnitudes and phase produced by T-F masking based beamforming. Strong separation performance has been observed on a spatialized reverberant version of the wsj0-2mix corpus.

DeLiang Wang

Papers

Detecting pitch of singing voice in polyphonic audio

A CASA-Based System for Long-Term SNR Estimation

Robust speaker recognition based on DNN/i-vectors and speech separation

Late Reverberation Suppression Using Recurrent Neural Networks with Long Short-Term Memory

Integrating Spectral and Spatial Features for Multi-Channel Speaker Separation.