论文标题
使用凸优化的稀疏和cosparse音频消除
Sparse and Cosparse Audio Dequantization Using Convex Optimization
论文作者
论文摘要
该论文显示了基于稀疏方法恢复量化信号的潜力。跟进Brauer等人的研究。 （IEEE ICASSP 2016），我们显着扩展了评估方案的范围：我们介绍了分析（Cosparse）模型，我们使用更有效的算法，我们尝试了另一种时频变换。本文表明，基于分析的模型与合成模型相当，但是Gabor变换比最初使用的余弦变换产生的结果更好。最后但并非最不重要的一点是，我们以可重复的方式提供代码和数据。
The paper shows the potential of sparsity-based methods in restoring quantized signals. Following up on the study of Brauer et al. (IEEE ICASSP 2016), we significantly extend the range of the evaluation scenarios: we introduce the analysis (cosparse) model, we use more effective algorithms, we experiment with another time-frequency transform. The paper shows that the analysis-based model performs comparably to the synthesis-model, but the Gabor transform produces better results than the originally used cosine transform. Last but not least, we provide codes and data in a reproducible way.
