Hearing Aid HA algorithms need to be tuned “fitted” to match the impairment of each specific patient. The lack of a fundamental HA fitting theory is a strong contributing factor to an unsatisfying sound experience for about 20 of HA patients. This paper proposes a probabilistic modeling approach to the design of HA algorithms. The proposed method relies on a generative probabilistic model for the hearing loss problem and provides for automated inference of the corresponding 1 signal processing algorithm, 2 the fitting solution as well as 3 a principled performance evaluation metric. All three tasks are realized as message passing algorithms in a factor graph representation of the generative model, which in principle allows for fast implementation on HA or mobile device hardware. The methods are theoretically worked out and simulated with a custom-built factor graph toolbox for a specific hearing loss model.
Factor analysis, principal component analysis, mixtures of gaussian clusters, vector quantization, Kalman filter models, and hidden Markov models can all be unified as variations of unsupervised learning under a single basic generative model. This is achieved by collecting together disparate observations and derivations made by many previous authors and introducing a new way of linking discrete and continuous state models using a simple nonlinearity. Through the use of other nonlinearities, we show how independent component analysis is also a variation of the same basic generative model.We show that factor analysis and mixtures of gaussians can be implemented in autoencoder neural networks and learned using squared error plus the same regularization term. We introduce a new model for static data, known as sensible principal component analysis, as well as a novel concept of spatially adaptive observation noise. We also review some of the literature involving global and local mixtures of the basic models and provide pseudocode for inference and learning for all the basic models. Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students. We address the problem of single-channel speech separation and recognition using loopy belief propagation in a way that enables efficient inference for an arbitrary number of speech sources. The graphical model consists of a set of N Markov chains, each of which represents a language model or grammar for a given speaker. A Gaussian mixture model with shared states is used to model the hidden acoustic signal for each grammar state of each source. The combination of sources is modeled in the log spectrum domain using non-linear interaction functions. Previously, temporal inference in such a model has been performed using an N-dimensional Viterbi algorithm that scales exponentially with the number of sources. In this paper, we describe a loopy message passing algorithm that scales linearly with language model size. The algorithm achieves human levels of performance, and is an order of magnitude faster than competitive systems for two speakers. Cristopher M. BishopInformation Science and StatisticsSpringer 2006, 738 pagesAs the author writes in the preface of the book, pattern recognition has its origin inengineering, whereas machine learning grew out of computer science. However, theseactivities can be viewed as two facets of the same ﬁeld, and they have undergonesubstantial development over the past years.Bayesian methods are widely used, while graphical models have emerged as a generalframework for describing and applying probabilistic models. Similarly new modelsbased on kernels have had signiﬁcant impact on both algorithms and applications.This textbook reﬂects these recent developments while providing a comprehensiveintroduction to the ﬁelds of pattern recognition and machine learning. It is aimedat advanced undergraduate or ﬁrst year PhD students, as well as researchers andpractitioners. It can be consider as an introductory course to the subject.The ﬁrst four chapters are devoted to the concepts of Probability and Statistics that areneededforreadingtherestofthebook,sowecanimaginethatthespeedishighinorderto get from zero to inﬁnity. I believe that it is better to study the book after a previouscourse on Probability and Statistics. On the other hand, a basic knowledge of linearalgebra and multivariate calculus is assumed.The other chapters give to a classic probabilist or statistician a point of view on someapplications that are very interesting but far from his usual world. In all the text themathematical aspects are at the second level in relation withthe ideas and intuitionsthatthe author wants to communicate.The book is supported by a great deal of additional material, including lecture slides aswell as the complete set of ﬁgures used in it, and the reader is encouraged to visit thebook web site for the latest information. So it can be very useful for a course or a talkabout the subject.