Flexible framework for audio reconstruction
This work addresses audio reconstruction challenges for signal processing applications, but it appears incremental as it extends existing concepts to transformed domains.
The paper tackles the problem of reconstructing audio signals from degraded observations in multiple domains, such as inpainting, declipping, and dequantization, by developing a unified framework and algorithmic solutions, demonstrating viability through an example with partial and quantized data.
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.