# Psychoacoustically Motivated Audio Declipping Based on Weighted l1 Minimization

## TL;DR

This paper introduces a psychoacoustically motivated audio declipping method that uses weighted l1 minimization, improving restoration quality by incorporating hearing thresholds and masking effects.

## Contribution

It presents a novel weighting scheme based on psychoacoustic models for sparse audio declipping, enhancing performance over existing methods.

## Key findings

- Improved SDR and PEMO-Q scores with proper weighting.
- Outperforms current state-of-the-art declipping techniques.
- Low computational complexity maintained.

## Abstract

A novel method for audio declipping based on sparsity is presented. The method incorporates psychoacoustic information by weighting the transform coefficients in the $\ell_1$ minimization. Weighting leads to an improved quality of restoration while retaining a low complexity of the algorithm. Three possible constructions of the weights are proposed, based on the absolute threshold of hearing, the global masking threshold and on a quadratic curve. Experiments compare the restoration quality according to the signal-to-distortion ratio (SDR) and PEMO-Q objective difference grade (ODG) and indicate that with correctly chosen weights, the presented method is able to compete, or even outperform, the current state of the art.