论文标题
多个互相关的身份比两个同性身份更好吗？改善盲目音频信号的时间差异的估计
Are Multiple Cross-Correlation Identities better than just Two? Improving the Estimate of Time Differences-of-Arrivals from Blind Audio Signals
论文作者
论文摘要
给定未知的音频源，可以使用盲信识别和利用互相关身份（CCI）来有效且鲁棒地求解了时间差异（TDOAS）的估计。先前的“盲”作品通过不同的算法解决方案和优化策略改善了TDOA的估计，同时始终坚持n = 2个麦克风。但是，如果我们可以通过增加n来直接提高性能，该怎么办？在本文中，我们试图研究这个方向，表明尽管有可争议的简单性，但这能够（急剧）改善基于CCI的最新盲信识别方法，而无需修改计算管道。受我们的结果的启发，我们试图通过铺平方法（以两种具体的，但初步的例子为示例）来为社区和从业人员进行热身，其中优化的进步与麦克风数量的增加相结合，以实现进一步的改进。
Given an unknown audio source, the estimation of time differences-of-arrivals (TDOAs) can be efficiently and robustly solved using blind channel identification and exploiting the cross-correlation identity (CCI). Prior "blind" works have improved the estimate of TDOAs by means of different algorithmic solutions and optimization strategies, while always sticking to the case N = 2 microphones. But what if we can obtain a direct improvement in performance by just increasing N? In this paper we try to investigate this direction, showing that, despite the arguable simplicity, this is capable of (sharply) improving upon state-of-the-art blind channel identification methods based on CCI, without modifying the computational pipeline. Inspired by our results, we seek to warm up the community and the practitioners by paving the way (with two concrete, yet preliminary, examples) towards joint approaches in which advances in the optimization are combined with an increased number of microphones, in order to achieve further improvements.
