# Deep Learning-Based Acoustic Echo Cancellation for Surround Sound Systems
## 1 Introduction  
Surround sound systems offer the potential for immersive sound field reproduction [1], enhancing realism in virtual reality and multimedia communication systems [2], such as immersive teleconference and acoustic human-machine interfaces. In a closed-loop teleconference system, the echo signal caused by the acoustic coupling between microphones and loudspeakers has a significant negative impact on hands-free speech communication systems. Conventional AEC methods often use adaptive filters to identify the acoustic echo paths, and the echo signal in each microphone is then estimated and subtracted from each microphone signal [3, 4]. However, the adaptive filtering-based algorithms may suffer from the well-known non-unique solution problem [5] due to high cross-correlation between the loudspeaker signals, when there are two or more reproduction channels. Although many algorithms [6, 7, 8, 9] have been proposed to decorrelate the loudspeaker signals so as to solve the non-unique solution problem, the reproduction quality and immersion of the far-end may be affected. Besides, as the number of channels increases, the computational complexity and convergence time will increase, and the control of the step size becomes much more sophisticated [10]. In recent years, deep learning-based methods have been employed in AEC and have achieved significant success. Compared with conventional AEC algorithms, deep learning-based AEC methods have the ability to recover the near-end signals from the microphone signals directly, and they do not need to identify the acoustic echo paths explicitly and also do not suffer from the non-unique solution problem. [PERSON] et al. [11] proposed a deep neural network (DNN)-based residual echo suppression gain estimation, whichwas then used to remove the nonlinear components after a linear acoustic echo canceller. [PERSON] et al. [12] formulated AEC as a supervised speech separation problem and proposed a recurrent neural network with bidirectional long short-term memory (BLSTM) to separate and suppress the far-end signal, hence removing the echo. However, due to the non-causality of BLSTM, the usage of this method may be restricted, especially in real-time applications. [PERSON] et al. [13] proposed a convolutional recurrent network (CRN) model to estimate the non-linear gain from the magnitude spectra of both the microphone and far-end signals for the stereo AEC, which was then multiplied by the spectrum of the microphone signal to estimate near-end speech. Besides, [PERSON] et al. [14] described a three-stage AEC and suppression framework for the ICASSP 2021 AEC Challenge, where the partitioned block frequency domain least mean square (PBFDLMS) with a time alignment was firstly implemented to cancel the linear echo components, and two deep learning networks were then proposed to suppress the residual echo and the non-speech residual noise simultaneously. In addition, [PERSON] et al. [15] proposed a neural cascade architecture, including a CRN module and an LSTM module, which is used for joint acoustic echo and noise suppression to address both single-channel and multi-channel AEC problems. More recently, [PERSON] et al. [16] proposed a deep complex multi-frame filtering network for stereophonic AEC, where two deep learning-based modules were separately used for suppression of the linear and residual echo components. Nowadays, with the widespread adoption of virtual reality and immersive teleconference, the surround sound-based real-time communication system will be popularized in the future trend. It is well known that the Ambisonic technique [17; 18; 19] is one basic and common surround sound recording and reproduction approach, where the Ambisonics encoder decomposes a sound field signal into spherical harmonics, i.e., B-format, and the Ambisonic decoder transforms the B-format signal into a multi-channel sound field signal, i.e., D-format, and they are then played back by multiple loudspeakers with a special layout, for example, the 5.1 channel surround sound layout.