On Adjusting the Learning Rate in Frequency Domain Echo Cancellation With Double-Talk

Abstract

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. In this paper we propose a new method of varying the learning rate of a frequency-domain echo canceller. This method is based on the derivation of the optimal learning rate of the NLMS algorithm in the presence of noise. The method is evaluated in conjunction with the multidelay block frequency domain (MDF) adaptive filter. We demonstrate that it performs better than current double-talk detection techniques and is simple to implement.

5 pages

Cited by in corpus (8)

• Acoustic Echo Cancellation by Combining Adaptive Digital Filter and Recurrent Neural Network
• Joint NN-Supported Multichannel Reduction of Acoustic Echo, Reverberation and Noise
• Interference-Normalised Least Mean Square Algorithm
• A New Robust Frequency Domain Echo Canceller With Closed-Loop Learning Rate Adaptation
• Joint Echo Cancellation and Noise Suppression based on Cascaded Magnitude and Complex Mask Estimation
• End-to-end Models with auditory attention in Multi-channel Keyword Spotting
• Sub-band Domain Multi-Hypothesis Acoustic Echo Canceler Based Acoustic Scene Analysis
• Joint Online Multichannel Acoustic Echo Cancellation, Speech Dereverberation and Source Separation