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.
Abstract (translated)
提出了一种基于稀疏度的音频解列方法。该方法通过加权$ell_1$最小化中的变换系数来合并心理声学信息。加权可以提高恢复质量，同时保持算法的低复杂性。基于绝对听阈、全局掩蔽阈和二次曲线，提出了三种可能的权值构造方法。实验根据信噪比（SDR）和PEMO-Q目标差分等级（ODG）对修复质量进行了比较，结果表明，在正确选择权值的情况下，该方法能够与现有技术进行竞争，甚至优于现有技术。
URL
https://arxiv.org/abs/1905.00628
