Generalized Wiener filtering with fractional power spectrograms
Generalized Wiener filtering with fractional power spectrograms
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DOI：
10.1109/icassp.2015.7177973
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发表时间：
2015-04
期刊：
影响因子：
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通讯作者：
A. Liutkus;R. Badeau
中科院分区：
文献类型：
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作者：
A. Liutkus;R. Badeau
近年来，许多研究集中于使用所谓的软掩蔽策略对独立波形进行单传感器分离，即混合的短期傅里叶变换被逐个元素地乘以频谱模型的比率。当信号是广义平稳的时，这种策略在理论上被证明是最优的维纳滤波：假设信号源的功率谱图相加得到混合物的功率谱图。然而，经验表明，改用分数谱图，如幅度，在实践中会产生良好的性能，因为它们在实验上更符合可加性假设。据我们所知，到目前为止还没有对这种过滤程序的概率解释。在这篇文章中，我们证明了为了建立软掩模，假设分数谱图的可加性可以理解为分离局部平稳的α稳定的可调和过程，简称α可调和过程，从而从理论上证明了这一过程。
In the recent years, many studies have focused on the single-sensor separation of independent waveforms using so-called soft-masking strategies, where the short term Fourier transform of the mixture is multiplied element-wise by a ratio of spectrogram models. When the signals are wide-sense stationary, this strategy is theoretically justified as an optimal Wiener filtering: the power spectrograms of the sources are supposed to add up to yield the power spectrogram of the mixture. However, experience shows that using fractional spectrograms instead, such as the amplitude, yields good performance in practice, because they experimentally better fit the additivity assumption. To the best of our knowledge, no probabilistic interpretation of this filtering procedure was available to date. In this paper, we show that assuming the additivity of fractional spectrograms for the purpose of building soft-masks can be understood as separating locally stationary α-stable harmonizable processes, α-harmonizable in short, thus justifying the procedure theoretically.