Popis anglicky
State-of-the-art methods for the reconstruction of degraded audio signals are successful at their performance. However, they still suffer from perceptually unpleasant or annoying artifacts coming from the reconstruction process. Only a few recent approaches involved psychoacoustics to alleviate this disturbing phenomena. Unfortunately, it turns out that the incorporation of auditory models into current methods it strongly limited. Their use therein is prevented by their complexity, non-differentiability and non-convexity. Recent results from the field of deep learning show that functionals can be trained to distinguish between faithful and implausible audio. Such discriminators come in the form of a neural network, thus being non-linear and non-convex, but, most importantly, differentiable. The project aims at using these discriminators as universal regularizers in algorithms inspired in convex optimization. This will not only lead to a general reconstruction framework, but also to significant improvements of perceptual quality in a wide range of audio inverse problems.

Klíčová slova
rekonstrukce signálu; regularizace; hluboké učení; neuronová síť; iterativní algoritmy; diskriminátor; kodek

Klíčová slova anglicky
signal processing, audio, signal reconstruction, regularization, deep learning, neural network, discriminator, iterative algorithms, auditory modeling, psychoacoustics