相似性与独立性感知波束形成器：使用幅度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
AI总结：
本文提出相似性与独立性感知波束形成器（SIBF），以粗略幅度谱图为参考，结合紧缩独立成分分析与相似性源模型，在 CHiME3 数据集上实现了优于 DNN 参考信号的目标源提取精度。
摘要：
本研究提出了一种新的源提取方法，称为相似性与独立性感知波束形成器（SIBF）。SIBF 使用粗略的幅度谱图作为参考信号来提取目标信号。与基于深度神经网络（DNN）的语音增强等目标增强方法生成的谱图相比，SIBF 的优势在于能够获得更准确的目标信号。在提取过程中，我们扩展了紧缩独立成分分析（deflationary independent component analysis）的框架，同时考虑参考信号与提取目标之间的相似性，以及所有潜在源之间的相互独立性。为了通过最大似然估计解决提取问题，我们引入了两种能够反映相似性的源模型类型。在 CHiME3 数据集上的实验结果表明，SIBF 提取的目标信号比 DNN 生成的参考信号更加准确。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
