The Theory of Matrices. By F R. Gantmacher. Two volumes, pp. 374 and 276. 1959. (Translated from the Russian by K. A. Hirsch; Chelsea Publishing Company, New York)

Authorship attribution supported by statistical or computational methods has a long history starting from the 19th century and is marked by the seminal study of Mosteller and Wallace (1964) on the authorship of the disputed “Federalist Papers.” During the last decade, this scientific field has been developed substantially, taking advantage of research advances in areas such as machine learning, information retrieval, and natural language processing. The plethora of available electronic texts (e.g., e-mail messages, online forum messages, blogs, source code, etc.) indicates a wide variety of applications of this technology, provided it is able to handle short and noisy text from multiple candidate authors. In this article, a survey of recent advances of the automated approaches to attributing authorship is presented, examining their characteristics for both text representation and text classification. The focus of this survey is on computational requirements and settings rather than on linguistic or literary issues. We also discuss evaluation methodologies and criteria for authorship attribution studies and list open questions that will attract future work in this area. © 2009 Wiley Periodicals, Inc.

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A survey of modern authorship attribution methods

Current speech enhancement techniques operate on the spectral domain and/or exploit some higher-level feature. The majority of them tackle a limited number of noise conditions and rely on first-order statistics. To circumvent these issues, deep networks are being increasingly used, thanks to their ability to learn complex functions from large example sets. In this work, we propose the use of generative adversarial networks for speech enhancement. In contrast to current techniques, we operate at the waveform level, training the model end-to-end, and incorporate 28 speakers and 40 different noise conditions into the same model, such that model parameters are shared across them. We evaluate the proposed model using an independent, unseen test set with two speakers and 20 alternative noise conditions. The enhanced samples confirm the viability of the proposed model, and both objective and subjective evaluations confirm the effectiveness of it. With that, we open the exploration of generative architectures for speech enhancement, which may progressively incorporate further speech-centric design choices to improve their performance.

SEGAN: Speech Enhancement Generative Adversarial Network

In this expository paper, we survey some of the latest developments in using preconditioned conjugate gradient methods for solving Toeplitz systems. One of the main results is that the complexity of solving a large class of $n$-by-$n$ Toeplitz systems is reduced to $O(n \log n)$ operations as compared to $O(n \log ^2 n)$ operations required by fast direct Toeplitz solvers. Different preconditioners proposed for Toeplitz systems are reviewed. Applications to Toeplitz-related systems arising from partial differential equations, queueing networks, signal and image processing, integral equations, and time series analysis are given.

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Conjugate Gradient Methods for Toeplitz Systems

Fast transversal filter (FTF) implementations of recursive-least-squares (RLS) adaptive-filtering algorithms are presented in this paper. Substantial improvements in transient behavior in comparison to stochastic-gradient or LMS adaptive algorithms are efficiently achieved by the presented algorithms. The true, not approximate, solution of the RLS problem is always obtained by the FTF algorithms even during the critical initialization period (first N iterations) of the adaptive filter. This true solution is recursively calculated at a relatively modest increase in computational requirements in comparison to stochastic-gradient algorithms (factor of 1.6 to 3.5, depending upon application). Additionally, the fast transversal filter algorithms are shown to offer substantial reductions in computational requirements relative to existing, fast-RLS algorithms, such as the fast Kalman algorithms of Morf, Ljung, and Falconer (1976) and the fast ladder (lattice) algorithms of Morf and Lee (1977-1981). They are further shown to attain (steady-state unnormalized), or improve upon (first N initialization steps), the very low computational requirements of the efficient RLS solutions of Carayannis, Manolakis, and Kalouptsidis (1983). Finally, several efficient procedures are presented by which to ensure the numerical Stability of the transversal-filter algorithms, including the incorporation of soft-constraints into the performance criteria, internal bounding and rescuing procedures, and dynamic-range-increasing, square-root (normalized) variations of the transversal filters.

Fast, recursive-least-squares transversal filters for adaptive filtering

A new computationally efficient algorithm for sequential least-squares (LS) estimation is presented in this paper. This fast a posteriori error sequential technique (FAEST) requires 5p MADPR (multiplications and divisions per recursion) for AR modeling and 7p MADPR for LS FIR filtering, where p is the number of estimated parameters. In contrast the well-known fast Kalman algorithm requires 8p MADPR for AR modeling and 10p MADPR for FIR filtering. The increased computational speed of the introduced algorithm stems from an alternative definition of the so-called Kalman gain vector, which takes better advantage of the relationships between forward and backward linear prediction.

A fast sequential algorithm for least-squares filtering and prediction

Speech enhancement from noise: a regenerative approach

Handwritten document image segmentation into text lines and words

In many signal processing applications, one often seeks the solution of a linear system of equations by means of fast algorithms. The special form of the matrix associated with the linear system may permit the development of algorithms requiring 0 (p2) or fewer operations. Hankel and Toeplitz matrices provide well known examples and various fast schemes have been developed in the literature to cover these cases. These techniques have common characteristics so that they may be generalized to cover a wider class of linear systems. The purpose of this paper is to develop fast algorithms that cover this wider set of systems. An important feature of the general scheme introduced here is that it leads to the definition of two broad classes of matrices, called diagonal innovation matrices (DIM) and peripheral innovation matrices (PIM), for which fast schemes can be developed. The class of PIM matrices includes many structures appearing in signal processing applications. Most of them are extensively studied in this paper and Fortran coding is provided. Finally, ARMA modeling is considered and within the general framework already introduced, fast methods for the determination of the autoregressive (AR) portion of the ARMA model are presented.

Fast recursive algorithms for a class of linear equations

This paper presents the Hellenic National (HNC), which is the corpus of Modern Greek developed by the Institute for Language and Speech Processing (ILSP). The presentation describes all stages of the creation of the corpus: collection of the material, tagging and tokenizing, construction of the database and the online implementation which aims at rendering the corpus accessible over Internet to the

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George Carayannis

Papers

A fast sequential algorithm for least-squares filtering and prediction

Speech enhancement from noise: a regenerative approach

Handwritten document image segmentation into text lines and words

Fast recursive algorithms for a class of linear equations

Design and implementation of the online ILSP Greek Corpus