一种新的鲁棒频域回声抵消器与闭环学习率适应
A New Robust Frequency Domain Echo Canceller With Closed-Loop Learning Rate Adaptation
AI总结：
本文提出一种基于多延迟块频域回声抵消器的闭环方法，通过学习率与对齐参数成正比，提升回声抵消性能，优于现有双工检测技术6dB。
摘要：
回声抵消的主要困难在于学习率需根据双工和回声路径变化而变化。本文提出一种新的闭环方法，学习率与对齐参数成正比，该参数基于梯度自适应方法估计。在多延迟块频域回声抵消器背景下，证明所提算法在双工检测方面优于现有方法达6dB。
英文摘要：
One of the main difficulties in echo cancellation is the fact that the learning rate needs to vary according to conditions such as double-talk and echo path change. Several methods have been proposed to vary the learning. In this paper we propose a new closed-loop method where the learning rate is proportional to a misalignment parameter, which is in turn estimated based on a gradient adaptive approach. The method is presented in the context of a multidelay block frequency domain (MDF) echo canceller. We demonstrate that the proposed algorithm outperforms current popular double-talk detection techniques by up to 6 dB.
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。
