论文标题
调查和对流行音频拒绝方法的广泛评估
A survey and an extensive evaluation of popular audio declipping methods
论文作者
论文摘要
信号处理中的动态范围限制通常会导致信号中的剪辑或饱和度。倾斜音频的任务是估计原始音频信号，鉴于其剪辑测量值，并且在近年来引起了极大的兴趣。倾向算法的音频通常会对基础信号（例如稀疏性或低级别）以及测量系统做出假设。在本文中，我们对文献中提出的算法进行了广泛的评论。对于每种算法，我们提出有关音频信号，建模域和优化算法的假设。此外，我们对实际音频数据提供了广泛的拒绝算法的广泛数值评估。我们根据信噪比的比例评估每种算法，并使用声音质量的感知指标。本文伴随着包含评估方法的存储库。
Dynamic range limitations in signal processing often lead to clipping, or saturation, in signals. The task of audio declipping is estimating the original audio signal, given its clipped measurements, and has attracted much interest in recent years. Audio declipping algorithms often make assumptions about the underlying signal, such as sparsity or low-rankness, and about the measurement system. In this paper, we provide an extensive review of audio declipping algorithms proposed in the literature. For each algorithm, we present assumptions that are made about the audio signal, the modeling domain, and the optimization algorithm. Furthermore, we provide an extensive numerical evaluation of popular declipping algorithms, on real audio data. We evaluate each algorithm in terms of the Signal-to-Distortion Ratio, and also using perceptual metrics of sound quality. The article is accompanied by a repository containing the evaluated methods.
