论文标题
音频倾向于（加权）分析社会稀疏
Audio Declipping with (Weighted) Analysis Social Sparsity
论文作者
论文摘要
我们开发了Siedenburg等人拒绝算法的流行音频的分析（COSPARSE）。 （2014）。此外，我们通过加权时频系数来扩展旧变体和新变体。我们检查了重量和收缩式操作员的几种组合的音频重建性能。在某些情况下，这些权重显示可改善重建质量；但是，在权重的帮助下，未加权方法获得的最佳分数不会超过。然而，分析经验维纳（EW）收缩能够达到计算更昂贵的竞争对手的质量，即持续的经验维纳（PEW）。此外，在审核动机的指标方面，提出的分析变体略微优于合成的变体。
We develop the analysis (cosparse) variant of the popular audio declipping algorithm of Siedenburg et al. (2014). Furthermore, we extend both the old and the new variants by the possibility of weighting the time-frequency coefficients. We examine the audio reconstruction performance of several combinations of weights and shrinkage operators. The weights are shown to improve the reconstruction quality in some cases; however, the best scores achieved by the non-weighted methods are not surpassed with the help of weights. Yet, the analysis Empirical Wiener (EW) shrinkage was able to reach the quality of a computationally more expensive competitor, the Persistent Empirical Wiener (PEW). Moreover, the proposed analysis variant incorporating PEW slightly outperforms the synthesis counterpart in terms of an auditorily motivated metric.
