Ronald W. Schafer

Bio: Ronald W. Schafer is an academic researcher from Hewlett-Packard. The author has contributed to research in topics: Speech processing & Digital signal processing. The author has an hindex of 17, co-authored 53 publications receiving 16192 citations. Previous affiliations of Ronald W. Schafer include Massachusetts Institute of Technology & Georgia Institute of Technology.

Quality assessment of audio: Increasing applicability scope of objective methods via prior identification of impairment types

Abstract: In this paper the design of a double-ended (intrusive) diagnostic tool for identifying five types of degradation in audio signals is reported. The impairment types taken into consideration are additive contamination with pink noise, occurrences of signal mutes, distortion by magnitude clipping, and the previous two types mixed with pink noise. As a simple solution to accomplish the established goal, a threshold-based hierarchical classification system is proposed, being completely defined from pre-processing of the input signals, passing through the estimation of a few characteristic features, up to data clustering criteria. Performance evaluation of the classifier is carried out via a validation database containing 60 impaired signals for each type of impairment, with five distinct degradation intensity levels. Considering the types and range of degradation levels considered in this work, excellent results are achieved, scoring above 96% of correctly classified data in the worst case. System performance in identifying mixed impairment types tends to deteriorate as the strength of the noise component increases.

Phase-Domain Statistical Analysis for Audio Source Localization

Abstract: We consider the problem of estimating the time-difference-of-arrival (TDOA) for audio source localization in noisy environments, defining a framework for statistical analysis in the phase domain which enables more reliable estimates. This is motivated by the fact that with complex sources, noise, and interfering signals, different frequency bands have significantly different signal-to-noise ratios, creating non-uniform distributions of errors in the phase measurements. Through a new method for analysis of the variations of the phase in frequency windows, we first estimate the signal-to-noise ratio for frequency, and then use it in a maximum-likelihood estimation of the time difference of arrival. We show that this corresponds to a generalization of the Phase Transform method (PHAT), and provides a theoretical justification of why it works so well. Numerical results show how the proposed technique compares favorably with PHAT.

DSP First (2nd Edition)

Abstract: 2 Sinusoids 9 2-1 Tuning-Fork Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2-2 Review of Sine and Cosine Functions . . . . . . . . . . . . . . . . . . . . 12 2-3 Sinusoidal Signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2-3.1 Relation of Frequency to Period . . . . . . . . . . . . . . . . . . . 15 2-3.2 Phase and Time Shift . . . . . . . . . . . . . . . . . . . . . . . . . 17 2-4 Sampling and Plotting Sinusoids . . . . . . . . . . . . . . . . . . . . . . . 19 2-5 Complex Exponentials and Phasors . . . . . . . . . . . . . . . . . . . . . 21 2-5.1 Review of Complex Numbers . . . . . . . . . . . . . . . . . . . . 22 2-5.2 Complex Exponential Signals . . . . . . . . . . . . . . . . . . . . 23 2-5.3 The Rotating Phasor Interpretation . . . . . . . . . . . . . . . . . . 25 2-5.4 Inverse Euler Formulas . . . . . . . . . . . . . . . . . . . . . . . . 27 2-6 Phasor Addition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2-6.1 Addition of Complex Numbers . . . . . . . . . . . . . . . . . . . . 29 2-6.2 Phasor Addition Rule . . . . . . . . . . . . . . . . . . . . . . . . . 29 2-6.3 Phasor Addition Rule: Example . . . . . . . . . . . . . . . . . . . 31 2-6.4 MATLAB Demo of Phasors . . . . . . . . . . . . . . . . . . . . . 33 2-6.5 Summary of the Phasor Addition Rule . . . . . . . . . . . . . . . . 33 2-7 Physics of the Tuning Fork . . . . . . . . . . . . . . . . . . . . . . . . . . 34 2-7.1 Equations from Laws of Physics . . . . . . . . . . . . . . . . . . . 34 2-7.2 General Solution to the Differential Equation . . . . . . . . . . . . 37 2-7.3 Listening to Tones . . . . . . . . . . . . . . . . . . . . . . . . . . 38 2-8 Time Signals: More Than Formulas . . . . . . . . . . . . . . . . . . . . . 38 2-9 Summary and Links . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 2-10 Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

Linear prediction: A tutorial review

Abstract: This paper gives an exposition of linear prediction in the analysis of discrete signals The signal is modeled as a linear combination of its past values and present and past values of a hypothetical input to a system whose output is the given signal In the frequency domain, this is equivalent to modeling the signal spectrum by a pole-zero spectrum The major part of the paper is devoted to all-pole models The model parameters are obtained by a least squares analysis in the time domain Two methods result, depending on whether the signal is assumed to be stationary or nonstationary The same results are then derived in the frequency domain The resulting spectral matching formulation allows for the modeling of selected portions of a spectrum, for arbitrary spectral shaping in the frequency domain, and for the modeling of continuous as well as discrete spectra This also leads to a discussion of the advantages and disadvantages of the least squares error criterion A spectral interpretation is given to the normalized minimum prediction error Applications of the normalized error are given, including the determination of an "optimal" number of poles The use of linear prediction in data compression is reviewed For purposes of transmission, particular attention is given to the quantization and encoding of the reflection (or partial correlation) coefficients Finally, a brief introduction to pole-zero modeling is given

Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition

Abstract: From the Publisher: This book takes an empirical approach to language processing, based on applying statistical and other machine-learning algorithms to large corpora.Methodology boxes are included in each chapter. Each chapter is built around one or more worked examples to demonstrate the main idea of the chapter. Covers the fundamental algorithms of various fields, whether originally proposed for spoken or written language to demonstrate how the same algorithm can be used for speech recognition and word-sense disambiguation. Emphasis on web and other practical applications. Emphasis on scientific evaluation. Useful as a reference for professionals in any of the areas of speech and language processing.

Permutation entropy: a natural complexity measure for time series.

Abstract: We introduce complexity parameters for time series based on comparison of neighboring values. The definition directly applies to arbitrary real-world data. For some well-known chaotic dynamical systems it is shown that our complexity behaves similar to Lyapunov exponents, and is particularly useful in the presence of dynamical or observational noise. The advantages of our method are its simplicity, extremely fast calculation, robustness, and invariance with respect to nonlinear monotonous transformations.

Techniques for data hiding

Abstract: Data hiding, a form of steganography, embeds data into digital media for the purpose of identification, annotation, and copyright. Several constraints affect this process: the quantity of data to be hidden, the need for invariance of these data under conditions where a "host" signal is subject to distortions, e.g., lossy compression, and the degree to which the data must be immune to interception, modification, or removal by a third party. We explore both traditional and novel techniques for addressing the data-hiding process and evaluate these techniques in light of three applications: copyright protection, tamper-proofing, and augmentation data embedding.