High-Order Sparse Linear Predictors for Audio Processing

Daniele Giacobello, Toon van Waterschoot, Mads Græsbøll Christensen, Søren Holdt Jensen, Marc Moonen

Publikation: Bidrag til tidsskriftKonferenceartikel i tidsskriftForskningpeer review

9 Citationer (Scopus)
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Linear prediction has generally failed to make a breakthrough in audio processing, as it has done in speech processing. This is mostly due to its poor modeling performance, since an audio signal is usually an ensemble of different sources. Nevertheless, linear prediction comes with a whole set of interesting features that make the idea of using it in audio processing not far fetched, e.g., the strong ability of modeling the spectral peaks that play a dominant role in perception. In this paper, we provide some preliminary conjectures and experiments on the use of high-order sparse linear predictors in audio processing. These predictors, successfully implemented in modeling the short-term and long-term redundancies present in speech signals, will be used to model tonal audio signals, both monophonic and polyphonic. We will show how the sparse predictors are able to model efficiently the different components of the spectrum of an audio signal, i.e., its tonal behavior and the spectral
envelope characteristic.
TidsskriftProceedings of the European Signal Processing Conference
Sider (fra-til)234-238
StatusUdgivet - 2010
BegivenhedEuropean Signal Processing Conference 2010 - Aalborg, Danmark
Varighed: 23 aug. 201027 aug. 2010


KonferenceEuropean Signal Processing Conference 2010

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