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Persistent Time-Frequency Shrinkage for Audio Denoising

In many audio processing applications, signals are represented by linear combinations of basis functions (such as with windowed Fourier transforms) that are collected in so-called dictionaries. These are considered well adapted to a particular class of signals if they lead to sparse representations, meaning only a small number of basis functions are required for good approximation of signals. Most natural signals have strong inherent structures, such as harmonics and transients, a fact that can be used for adapting audio processing algorithms. This paper considers the audio-denoising problem from the perspective of structured sparse representation. A generalized thresholding scheme is presented from which simple audio-denoising operators are derived. They perform equally well compared to state-of-the-art methods while featuring significantly less computational costs.

Authors: Siedenburg, Kai; Dörfler, Monika
Affiliations: Center for Interdisciplinary Research in Music Media and Technology (CIRMMT), Schulich School of Music, McGill University, Montreal, Canada; Austrian Research Institute for Artificial Intelligence, Vienna, Austria; Numerical Harmonic Analysis Group, Faculty of Mathematics, University of Vienna, Vienna, Austria(See document for exact affiliation information.)
JAES Volume 61 Issue 1/2 pp. 29-38; January 2013 Import into BibTeX

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