基于迭代投射和Boost启动的相似性和独立性波束形成器
Similarity-and-Independence-Aware Beamformer with Iterative Casting and Boost Start for Target Source Extraction Using Reference
摘要：
目标源提取对于提高人的语音可懂度和计算机的语音识别性能具有重要意义。本文提出了一种基于相似性和独立性感知的目标源提取方法。SIBF使用粗略的幅度谱图作为参考信号来提取目标源。SIBF的优势在于，与基于深度神经网络的语音增强等目标增强方法生成的频谱图相比，它可以获得更准确的信号。对于提取，我们扩展了通货紧缩的独立成分分析（ICA）的框架，通过考虑参考和提取的目标源之间的相似性，除了所有的潜在源的相互独立性。为了解决最大似然估计的提取问题，我们引入了三种能够反映相似性的源模型。本研究的主要贡献如下。首先，使用两种方法提高提取性能，即加速启动以实现更快的收敛和迭代铸造以生成更准确的参考。通过CHiME3数据集的实验验证了这些方法的有效性。第二，一个固定点的概念有关的准确性。这一概念有助于理解基准电压源和SIBF输出在精度方面的关系。第三，实现了SIBF和基于掩模的波束形成器的统一制定，以将传统BF的专业知识应用于SIBF。本文的研究结果也可以提高SIBF的性能，促进ICA和传统波束形成器的研究。索引词：波束形成器，独立分量分析，源分离，语音增强，目标源提取
英文摘要：
Target source extraction is significant for improving human speech intelligibility and the speech recognition performance of computers. This study describes a method for target source extraction, called the similarity-and-independence-aware beamformer (SIBF). The SIBF extracts the target source using a rough magnitude spectrogram as the reference signal. The advantage of the SIBF is that it can obtain a more accurate signal than the spectrogram generated by target-enhancing methods such as speech enhancement based on deep neural networks. For the extraction, we extend the framework of deflationary independent component analysis (ICA) by considering the similarities between the reference and extracted target sources, in addition to the mutual independence of all the potential sources. To solve the extraction problem by maximum-likelihood estimation, we introduce three source models that can reflect the similarities. The major contributions of this study are as follows. First, the extraction performance is improved using two methods, namely boost start for faster convergence and iterative casting for generating a more accurate reference. The effectiveness of these methods is verified through experiments using the CHiME3 dataset. Second, a concept of a fixed point pertaining to accuracy is developed. This concept facilitates understanding the relationship between the reference and SIBF output in terms of accuracy. Third, a unified formulation of the SIBF and mask-based beamformer is realized to apply the expertise of conventional BFs to the SIBF. The findings of this study can also improve the performance of the SIBF and promote research on ICA and conventional beamformers. Index Terms: beamformer, independent component analysis, source separation, speech enhancement, target source extraction
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。
