Flexible framework for audio reconstruction
arXiv:2004.11162
Abstract
The paper presents a unified, flexible framework for the tasks of audio inpainting, declipping, and dequantization. The concept is further extended to cover analogous degradation models in a transformed domain, e.g. quantization of the signal's time-frequency coefficients. The task of reconstructing an audio signal from degraded observations in two different domains is formulated as an inverse problem, and several algorithmic solutions are developed. The viability of the presented concept is demonstrated on an example where audio reconstruction from partial and quantized observations of both the time-domain signal and its time-frequency coefficients is carried out.
References in corpus (5)
- Sparse Recovery and Dictionary Learning from Nonlinear Compressive Measurements
- Introducing SPAIN (SParse Audio INpainter)
- Revisiting Synthesis Model of Sparse Audio Declipper
- Psychoacoustically Motivated Audio Declipping Based on Weighted l1 Minimization
- A modeling and algorithmic framework for (non)social (co)sparse audio restoration