Deep Echo Path Modeling for Acoustic Echo Cancellation

Acoustic echo cancellation (AEC) is a key audio processing technology that removes echoes from microphone inputs to enable natural-sounding full-duplex communication. In recent years, deep learning has shown great potential for advancing AEC. However, deep learning methods face challenges in generalizing to complex environments, especially unseen conditions not represented in training. In this paper, we propose a deep learning-based method to predict the echo path in the time-frequency domain. Specifically, we first estimate the echo path under single-talk scenario without near-end signal and then utilize these predicted echo paths as auxiliary labels to train the model on double-talk scenario with near-end signal. Experimental results show that our method outperforms the strong baselines and exhibits good generalization capabilities for unseen acoustic scenarios. By estimating the echo path using deep learning, this work advances AEC performance in the presence of complex conditions.