Convolutive and under-determined blind audio source separation from noisy recordings is a challenging problem. Several computational strategies have been proposed to address this problem. This study is concerned with several modifications to the expectation-minimization-based algorithm, which iteratively estimates the mixing and source parameters. This strategy assumes that any entry in each source spectrogram is modeled using superimposed Gaussian components, which are mutually and individually independent across frequency and time bins. In our approach, we resolve this issue by considering a locally smooth temporal and frequency structure in the power source spectrograms. Local smoothness is enforced by incorporating a Gibbs prior in the complete data likelihood function, which models the interactions between neighboring spectrogram bins using a Markov random field. Simulations using audio files derived from stereo audio source separation evaluation campaign 2008 demonstrate high efficiency with the proposed improvement.

中文翻译：

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使用 MRF 平滑改进卷积和不确定的盲音频源分离。

从嘈杂的录音中分离出卷积和不确定的盲音频源是一个具有挑战性的问题。已经提出了几种计算策略来解决这个问题。本研究涉及对基于期望最小化的算法的若干修改，该算法迭代地估计混合和源参数。该策略假设每个源频谱图中的任何条目都使用叠加的高斯分量建模，这些分量在频率和时间段上相互独立且独立。在我们的方法中，我们通过考虑电源频谱图中局部平滑的时间和频率结构来解决这个问题。通过在完整的数据似然函数中加入 Gibbs 先验来强制执行局部平滑，它使用马尔可夫随机场对相邻频谱图箱之间的相互作用进行建模。使用源自 2008 年立体声音频源分离评估活动的音频文件的模拟证明了拟议改进的高效率。