Analysis, visualization, and transformation of audio signals using dictionary-based methods

This article provides an overview of dictionary-based methods (DBMs), and reviews recent work in the application of such methods to working with audio and music signals. As Fourier analysis is to additive synthesis, DBMs can be seen as the analytical counterpart to a generalized granular synthesis, where a sound is built by combining heterogeneous atoms selected from a user-defined dictionary. As such, DBMs provide novel ways for analyzing and visualizing audio signals, creating multiresolution descriptions of their contents, and designing sound transformations unique to a description of audio in terms of atoms. 1

Analysis, Visualization, and Transformation of Audio Signals Using Dictionary-based Methods

date-added: 2014-01-07 09:15:58 +0000 date-modified: 2014-01-07 09:15:58 +0000date-added: 2014-01-07 09:15:58 +0000 date-modified: 2014-01-07 09:15:58 +000

Probability, random variables, and random processes: theory and signal processing applications

Probability, Random Variables, and Random Processes is a comprehensive textbook on probability theory for engineers that provides a more rigorous mathematical framework than is usually encountered in undergraduate courses. It is intended for first-year graduate students who have some familiarity with probability and random variables, though not necessarily of random processes and systems that operate on random signals. It is also appropriate for advanced undergraduate students who have a strong mathematical background. The book has the following features: Several ap

Interference-Driven Adaptation in Sparse Approximation

Abstract—Sparse approximation attempts to find an efficient signal representation by adaptively building a signal vector space from elements of a usually redundant and overcomplete dictionary of atoms. Often, however, the representations produced by iterative descent methods, such as orthogonal matching pursuit (OMP), will contain atoms that are poorly chosen and are later confused to be features of the signal. Poorly selected atoms bring about the selection other atoms that serve to correct for previous choices using destructive interference. This behavior diminishes the efficiency of a representation. In this paper, we propose and study a modification of the atom selection in OMP that takes into account the aforementioned effects. We find that a pursuit adapting to the interference between atoms can create a more efficient representation than that created by OMP. The representations created are more a representation of the signal and its features and less a reflection of the decomposition process. I

Efficient AOA Estimation Techniques for GPS Signal

ITC/USA 2015 Conference Proceedings / The Fifty-First Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2015 / Bally's Hotel & Convention Center, Las Vegas, NVGlobal Positioning System (GPS) interference signals are suppressed using angle-of-arrival (AOA) techniques, while at the same time the power of the GPS signal is enhanced. After estimating all AOAs from the received signal, we must determine which AOA corresponds to the GPS signal of interest, and in the presence of high-power interference signals. In this paper, we describe an algorithm for selecting the GPS AOA by first comparing all AOAs derived from the received signals before despreading. Although this approach has excellent performance, it has a high computational complexity. In order to overcome this drawback, we introduce a modification that yields an efficient GPS AOA estimation algorithm, which is based on a modified despreader and the constant modulus (CM) array cost function. The CM array is capable of selecting signals that have a constant modulus while rejecting non-CM interference signals. The modified despreader is the mechanism that allows this to be achieved, where unlike the interference signals, the GPS signal of interest maintains a constant modulus.International Foundation for TelemeteringProceedings from the International Telemetering Conference are made available by the International Foundation for Telemetering and the University of Arizona Libraries. Visit http://www.telemetry.org/index.php/contact-us if you have questions about items in this collection

Agglomerative clustering in sparse atomic decompositions of audio signals

We present a correlation-based algorithm for the agglomerative clus-tering of atoms in sparse atomic decompositions of audio signals. Our goal is to demonstrate useful relationships between elements of the decomposition and the content of the original signal, for such purposes as analysis and modification. We evaluate the performance of the agglomeration algorithm using decompositions of synthetic and real audio signals, and discuss possible extensions of this work. Index Terms — Clustering methods, signal analysis, signal res-olution, time-frequency analysis. 1