Abstract
Within the last few decades, several new signal processing tools have appeared. These have mainly been compared using constructed signals, signals designed to show the advantage of a new method over already existing methods. We evaluate the following methods on real signals: basis pursuit; minimum fuel neural networks; matching pursuit; best orthogonal basis; alternating projections; methods of frames. The methods are applied on a number of excerpts sampled from a small collection of music, and their ability to express music signals in a sparse manner is evaluated. The sparseness is measured by a number of sparseness measures and results are shown on the /spl lscr/ /sup 1/ norm of the coefficients, using a dictionary containing a Dirac basis, a discrete cosine transform, and a wavelet packet. Evaluated only on sparseness, matching pursuit is the best method, and it is also relatively fast.
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