Deep Learning-Based Acoustic Echo Cancellation for Surround Sound Systems

Abstract

Surround sound systems that play back multi-channel audio signals through multiple loudspeakers can improve augmented reality, which has been widely used in many multimedia communication systems. It is common that a hand-free speech communication system suffers from the acoustic echo problem, and the echo needs to be canceled or suppressed completely. This paper proposes a deep learning-based acoustic echo cancellation (AEC) method to recover the desired near-end speech from the microphone signals in surround sound systems. The ambisonics technique was adopted to record the surround sound for reproduction. To achieve a better generalization capability against different loudspeaker layouts, the compressed complex spectra of the first-order ambisonic signals (B-format) were sent to the neural network as the input features directly instead of using the ambisonic decoded signals (D-format). Experimental results on both simulated and real acoustic environments showed the effectiveness of the proposed algorithm in surround AEC, and outperformed other competing methods in terms of the speech quality and the amount of echo reduction.