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Audio Inpainting
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| Citations: | 10 - 0 self |
BibTeX
@MISC{Adler_audioinpainting,
author = {Amir Adler and Valentin Emiya and Maria G. Jafari and Michael Elad and Rémi Gribonval and Mark D. Plumbley},
title = {Audio Inpainting},
year = {}
}
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Abstract
We propose the Audio Inpainting framework that recovers portions of audio data distorted due to impairments such as impulsive noise, clipping, and packet loss. In this framework, the distorted data are treated as missing and their location is assumed to be known. The signal is decomposed into overlapping timedomain frames and the restoration problem is then formulated as an inverse problem per audio frame. Sparse representation modeling is employed per frame, and each inverse problem is solved using the Orthogonal Matching Pursuit algorithm together with a discrete cosine or a Gabor dictionary. The Signal-to-Noise Ratio performance of this algorithm is shown to be comparable or better than state-of-the-art methods when blocks of samples of variable durations are missing. We also demonstrate that the size of the block of missing samples, rather than the overall number of missing samples, is a crucial parameter for high quality signal restoration. We further introduce a constrained Matching Pursuit approach for the special case of audio declipping that exploits the sign pattern of clipped audio samples and their maximal absolute value, as well as allowing the user to specify the maximum amplitude of the signal. This approach is shown to outperform state-of-the-art and commercially available methods for audio declipping in terms of Signal-to-Noise Ratio. Index Terms—Inpainting, clipping, sparse representation, matching pursuit.
Keyphrases
audio inpainting inverse problem audio declipping clipped audio sample sparse representation modeling variable duration sign pattern special case constrained matching pursuit approach signal-to-noise ratio maximal absolute value index term inpainting discrete cosine signal-to-noise ratio performance impulsive noise gabor dictionary audio data orthogonal matching pursuit restoration problem available method audio inpainting framework audio frame overall number high quality signal restoration timedomain frame maximum amplitude sparse representation state-of-the-art method packet loss crucial parameter
