Audio inpainting: Evaluation of time-frequency representations and structured sparsity approaches

  • Audio signals such as music are known to exhibit distinct and sparse time-frequency patterns. In particular, the short-time Fourier/Gabor transform is widely used for sparsely representing audio signals. In this contribution, such sparsity patterns are exploited to reconstruct missing samples. The quality of reconstruction is evaluated for various kinds of proximal splitting algorithms, time-frequency discretizations and sparsity enforcing constraints. Furthermore, given a time-frequency representation, we investigate the performance of synthesis vs. analysis approaches to the reconstruction problem. The equidistant discretization scheme of Gabor transforms is non-adaptive. It is plausible, that more flexible time-frequency representations like wavelets or ERBlets improve reconstruction of missing audio samples. The numerical results presented in this contribution confirm this conjecture and lead to good reconstruction results even for gaps of contiguously missing samples, whose size is notably larger than reported for inpainting experiments carried out previously.

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Metadaten
Author:Florian Lieb, Hans-Georg Stark
URL:https://doi.org/10.1016/j.sigpro.2018.07.012
Parent Title (English):Signal Processing
Document Type:Article
Language:English
Year of Completion:2018
Date of first Publication:2018/12/01
Release Date:2018/09/26
Tag:Audio inpainting, Structured sparsity, Proximal splitting, Synthesis/analysis, Non-stationary Gabor frames
GND Keyword:Zeit-Frequenz-Darstellung; Tonsignal
Volume:2018
Issue:153
First Page:291
Last Page:299
Urheberrecht:0
research focus :Intelligent Systems / Artifical Intelligence and Data Science
Intelligent Systems / Intelligent Sensors and Signals
Licence (German):Keine Lizenz - es gilt das deutsche Urheberrecht
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