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Multiresolution Sinusoidal Modeling

  • Michael M. Goodwin
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 467)

Abstract

As indicated in the previous chapter, the standard sinusoidal model has difficulty modeling broadband processes — both noiselike components and timelocalized transient events such as attacks. Such broadband processes thus appear in the residual of the sinusoidal analysis-synthesis. A perceptual model for noiselike components will be presented in Chapter 4; that representation, however, is inadequate for time-localized events such as attack artifacts, so it is necessary to consider ways to prevent these events from appearing in the residual. In this chapter, the sinusoidal model is reinterpreted in terms of expansion functions; the structure of these expansion functions both indicates why the model breaks down for time-localized events and suggests methods to improve the model by casting it in a multiresolution framework. Two approaches are considered: applying the sinusoidal model to filter bank subbands, and using signal-adaptive analysis and synthesis frame sizes. These specific methods are discussed after a consideration of multiresolution as exemplified by the discrete wavelet transform.

Keywords

Discrete Wavelet Filter Bank Wavelet Packet Dynamic Algorithm Perfect Reconstruction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1998

Authors and Affiliations

  • Michael M. Goodwin
    • 1
  1. 1.University of CaliforniaBerkeleyUSA

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