论文标题
相似性和独立感知的波束形式：使用幅度光谱图作为参考的目标源提取方法
Similarity-and-Independence-Aware Beamformer: Method for Target Source Extraction using Magnitude Spectrogram as Reference
论文作者
论文摘要
这项研究提出了一种用于源提取的新方法，称为相似性和独立的波束形式（SIBF）。 SIBF使用粗糙的幅度光谱图作为参考信号提取目标信号。 SIBF的优点是，与基于深层神经网络（DNNS）（DNNS）（DNNS）产生的频谱相比，它可以获得准确的目标信号。对于提取，我们通过考虑参考和提取的目标之间的相似性以及所有潜在来源的相互独立性，扩展了通缩独立组件分析的框架。为了通过最大可能的估计来解决提取问题，我们介绍了两种可以反映相似性的源模型类型。 Chime3数据集的实验结果表明，SIBF提取的目标信号比DNN产生的参考信号更准确。 索引术语：半框源分离，相似性和独立感知的波束形成器，放流独立组件分析，源模型
This study presents a novel method for source extraction, referred to as the similarity-and-independence-aware beamformer (SIBF). The SIBF extracts the target signal using a rough magnitude spectrogram as the reference signal. The advantage of the SIBF is that it can obtain an accurate target signal, compared to the spectrogram generated by target-enhancing methods such as the speech enhancement based on deep neural networks (DNNs). For the extraction, we extend the framework of the deflationary independent component analysis, by considering the similarity between the reference and extracted target, as well as the mutual independence of all potential sources. To solve the extraction problem by maximum-likelihood estimation, we introduce two source model types that can reflect the similarity. The experimental results from the CHiME3 dataset show that the target signal extracted by the SIBF is more accurate than the reference signal generated by the DNN. Index Terms: semiblind source separation, similarity-and-independence-aware beamformer, deflationary independent component analysis, source model
