Sparse Reverberant Audio Source Separation via Reweighted Analysis
Sparse Reverberant Audio Source Separation via Reweighted Analysis
复制标题
DOI：
10.1109/tasl.2013.2250962
复制
发表时间：
2013-07
期刊：
影响因子：
--
通讯作者：
S. Arberet;P. Vandergheynst;R. Carrillo;J. Thiran;Y. Wiaux
中科院分区：
文献类型：
--
作者：
S. Arberet;P. Vandergheynst;R. Carrillo;J. Thiran;Y. Wiaux
本文提出了一种基于欠定卷积混合滤波器的源信号估计新算法。大多数最先进的方法是处理消声或短混响混合，假设在时间-频率域中的合成稀疏先验和卷积混合过程的窄带近似。在本文中，我们解决卷积混合源估计与一个新的算法的基础上，i）分析稀疏先验，ii）重新加权计划，以增加稀疏性，iii）宽带数据保真度项的约束形式。我们表明，通过理论讨论和仿真，该算法是特别适合于现实混响混合源分离。特别是，该算法优于国家的最先进的方法混响混合音频源的BSS Oracle数据集上的信号失真比超过2 dB。
We propose a novel algorithm for source signals estimation from an underdetermined convolutive mixture assuming known mixing filters. Most of the state-of-the-art methods are dealing with anechoic or short reverberant mixture, assuming a synthesis sparse prior in the time-frequency domain and a narrowband approximation of the convolutive mixing process. In this paper, we address the source estimation of convolutive mixtures with a new algorithm based on i) an analysis sparse prior, ii) a reweighting scheme so as to increase the sparsity, iii) a wideband data-fidelity term in a constrained form. We show, through theoretical discussions and simulations, that this algorithm is particularly well suited for source separation of realistic reverberation mixtures. Particularly, the proposed algorithm outperforms state-of-the-art methods on reverberant mixtures of audio sources by more than 2 dB of signal-to-distortion ratio on the BSS Oracle dataset.