Adaptive Signal Models: Theory, Algorithms, and Audio by Michael M. Goodwin

By Michael M. Goodwin

Adaptive sign types: conception, Algorithms and Audio Applications provides tools for deriving mathematical versions of usual signs. The creation covers the basics of analysis-synthesis platforms and sign representations. a few of the issues within the advent comprise excellent and near-perfect reconstruction, the excellence among parametric and nonparametric equipment, the function of compaction in sign modeling, simple and overcomplete sign expansions, and time-frequency solution matters. those issues come up during the e-book as do a couple of different issues corresponding to filter out banks and multiresolution.
the second one bankruptcy supplies an in depth improvement of the sinusoidal version as a parametric extension of the short-time Fourier remodel. This ends up in multiresolution sinusoidal modeling concepts in bankruptcy 3, the place wavelet-like ways are merged with the sinusoidal version to yield greater types. In bankruptcy 4, the analysis-synthesis residual is taken into account; for real looking synthesis, the residual needs to be individually modeled after coherent parts (such as sinusoids) are got rid of. The residual modeling method relies on psychoacoustically prompted nonuniform clear out banks. bankruptcy 5 bargains with pitch-synchronous models of either the wavelet and the Fourier remodel; those let for compact versions of pseudo-periodic signs. bankruptcy Six discusses fresh algorithms for deriving sign representations in response to time-frequency atoms; essentially, the matching pursuit set of rules is reviewed and prolonged.
The sign versions mentioned within the publication are compact, adaptive, parametric, time-frequency representations which are beneficial for research, coding, amendment, and synthesis of ordinary indications reminiscent of audio. The types are all interpreted as tools for decomposing a sign when it comes to basic time-frequency atoms; those interpretations, in addition to the adaptive and parametric natures of the types, serve to hyperlink some of the tools handled within the textual content.
Adaptive sign versions: idea, Algorithms and Audio Applications serves as a good reference for researchers of sign processing and should be used as a textual content for complicated classes at the topic.

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Extra resources for Adaptive Signal Models: Theory, Algorithms, and Audio Applications

Example text

Perfect reconstruction can be achieved in this case; the various artifacts cancel in the synthesis. g. quantized, the subband artifacts will not be properly cancelled and artifacts will appear in the final synthesis. In pseudo-periodic musical signals, the frequencies of the harmonics vary as the pitch evolves in time; in such cases, it is intuitively desirable that the sum-of-partials model should be an aggregation of chirps whose frequencies are coupled while changing in time in a complex way.

Overcomplete expansions can be similarly parametric in nature if the underlying dictionary has a meaningful parametric structure. In such cases, the traditional distinction between parametric and nonparametric methods evaporates, and the over complete expansion provides a highly useful signal model. Nonlinear analysis. In each model, the model estimation is inherently nonlinear. The sinusoidal and pitch-synchronous models rely on nonlinear parameter estimation and interpolation. The matching pursuit is inherently nonlinear in the way it selects the expansion functions from the overcomplete dictionary; it overcomes the inadequacies of linear methods such as the SVD while providing for successive refinement and compact sparse approximations.

Chapter 5 examines pitch-synchronous sinusoidal models and wavelet transforms; estimation of the pitch parameter is shown to provide a useful avenue for improving the signal representation in both cases. In Chapter 6, overcomplete expansions are revisited; signal modeling is interpreted as an inverse problem and connections between structured overcomplete expansions and parametric methods are considered. The chapter discusses the matching pursuit algorithm for computing overcomplete expansions, and considers overcomplete dictionaries based on damped sinusoids, for which expansions can be computed using simple recur- 28 ADAPTIVE SIGNAL MODELS sive filter banks.

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