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

Abstract

In this article we provide an overview of dictionary-based methods (DBMs) -also called sparse approximation -and review recent work in the application of such methods to working with signals, in particular audio and music signals. As Fourier analysis is to additive synthesis, DBMs can be seen as the analytical counterpart to granular synthesis since a signal is rebuilt by a linear combination of heterogeneous atoms selected from a user-defined dictionary. We demonstrate how DBMs provide novel means for analyzing, visualizing, and transforming audio signals by creating multiresolution and parametric descriptions of their contents.

High attenuation electromagnetic interface filter for effective processing of audio signals

“…It is possible to determine the characteristics of any device that emits signals, such as a speaker, antenna, or any other source of signals, based on their emitted signals. Signals are filtered to isolate specific features using filters [23].…”