The matching pursuit algorithm can be used to derive signal decompositions in terms of the elements of a dictionary of time-frequency atoms. Using a structured overcomplete dictionary yields a signal model that is both parametric and signal adaptive. In this paper, we apply matching pursuit to the derivation of signal expansions based on damped sinusoids. It is shown that expansions in terms of complex damped sinusoids can be efficiently derived using simple recursive filter banks. We discuss a subspace extension of the pursuit algorithm that provides a framework for deriving real-valued expansions of real signals based on such complex atoms. Furthermore, we consider symmetric and asymmetric two-sided atoms constructed from underlying one-sided damped sinusoids. The primary concern is the application of this approach to the modeling of signals with transient behavior such as music; it is shown that time-frequency atoms based on damped sinusoids are more suitable for representing transients than symmetric Gabor atoms. The resulting atomic models are useful for signal coding and analysis modification synthesis.
Spatial audio coding and enhancement address the growing commercial need to store and distribute multichannel audio and to render content optimally on arbitrary reproduction systems. In this paper, we discuss a spatial analysis-synthesis scheme which applies principal component analysis to an STFT-domain representation of the original audio to separate it into primary and ambient components, which are then respectively analyzed for cues that describe the spatial percept of the audio scene on a per-tile basis; these cues are used by the synthesis to render the audio appropriately on the available playback system. The proposed framework can be tailored for robust spatial audio coding, or it can be applied directly to enhancement scenarios where there are no rate constraints on the intermediate spatial data and audio representation.
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