Two-step Band-split Neural Network Approach for Full-band Residual Echo
  Suppression
This paper describes a Two-step Band-split Neural Network (TBNN) approach for
full-band acoustic echo cancellation. Specifically, after linear filtering, we
split the full-band signal into wide-band (16KHz) and high-band (16-48KHz) for
residual echo removal with lower modeling difficulty. The wide-band signal is
processed by an updated gated convolutional recurrent network (GCRN) with U$^2$
encoder while the high-band signal is processed by a high-band post-filter net
with lower complexity. Our approach submitted to ICASSP 2023 AEC Challenge has
achieved an overall mean opinion score (MOS) of 4.344 and a word accuracy
(WAcc) ratio of 0.795, leading to the 2$^{nd}$ (tied) in the ranking of the
non-personalized track.
Zihan Zhang, Shimin Zhang, Mingshuai Liu, Yanhong Leng, Zhe Han, Li Chen, Lei Xie
# Two-Step Band-Split Neural Network Approach for Full-Band Residual Echo Suppression

###### Abstract

This paper describes a Two-step Band-split Neural Network (TBNN) approach for full-band acoustic echo cancellation. Specifically, after linear filtering, we split the full-band signal into wide-band (16KHz) and high-band (16-48KHz) for residual echo removal with lower modeling difficulty. The wide-band signal is processed by an updated gated convolutional recurrent network (GCRN) with U\({}^{2}\) encoder while the high-band signal is processed by a high-band post-filter net with lower complexity. Our approach submitted to ICASSP 2023 AEC Challenge has achieved an overall mean opinion score (MOS) of 4.344 and a word accuracy (WAcc) ratio of 0.795, leading to the 2\({}^{nd}\) (tied) in the ranking of the non-personalized track.

Zihan Zhang\({}^{1}\), Shimin Zhang\({}^{1}\), Mingshuai Liu\({}^{1}\), Yanhong Leng\({}^{2}\), Zhe Han\({}^{2}\), Li Chen\({}^{2}\), Lei Xie\({}^{1,*}\)\({}^{1}\)Audio, Speech and Language Processing Group (ASLP@NPU), School of Computer Science, Northwestern Polytechnical University, Xi'an, China

\({}^{2}\)ByteDance, China Acoustic echo cancellation, noise suppression, band-split, two-step network.

## 1 Introduction

The 4th acoustic echo cancellation (AEC) challenge [1] is a flagship event of the ICASSP 2023 signal processing grand challenge, with the aim to benchmark AEC techniques in real-time full-band (48KHz) speech communication. In this challenge, our team has submitted a _hybrid_ approach that combines a linear filter with a neural post-filter to the non-personalized AEC track (i.e., without using target speaker embedding). Since noise-like components usually dominate in higher bands of speech and structured harmonics are mainly found in wide-band signals, we propose a two-step band-split neural network (TBNN) approach to particularly handle full-band residual echo removal on wide-band (16KHz) and high-band (16-48KHz) in a two-step process, as an extension of our previous work [2] to better model full-band signals with low complexity. Specifically, the wide-band poster filter (WBPF) is based on the gated convolutional recurrent network (GCRN) [2] but with upgraded U\({}^{2}\) encoder [3] for better latent feature extraction. We also redesign the data simulation method and the loss function to accommodate full-band signals. According to the results, our system has ranked 2nd place (tied rank) in the non-personalized track with an overall mean opinion score (MOS) of 4.344 and a word accuracy (WAcc) ratio of 0.795.

## 2 Proposed Method

### Problem formulation

For a typical full-duplex communication system consisting of a microphone and a loudspeaker, the signal model can be expressed as

\[d(n)=s(n)+r(n)+v(n)+z(n) \tag{1}\] \[z(n)=h(n)*\mathcal{F}\{x(n)\} \tag{2}\]

where the microphone signal \(d(n)\) is composed of the near-end speech signal \(s(n)\) which may include early reflections, late reverberation \(r(n)\), additive noise \(v(n)\), and echo signal \(z(n)\), where \(n\) is the sampling index. The echo signal \(z(n)\) is obtained by convolving the echo path \(h(n)\) with the nonlinear distorted reference signal \(x(n)\), shown in Eq. (2), where \(\mathcal{F}\) refers to the nonlinear distortions, \(*\) refers to convolution. An AEC system aims to cancel \(z(n)\) from \(d(n)\) given \(x(n)\). For complex real-life scenarios, noise \(v(n)\) and reverberation \(r(n)\) also need to be suppressed.

We adopt a time delay estimation (TDE) module based on the sub-band cross-correlation to align the reference signal. Our linear filter uses a sub-band adaptive filtering method based on the NLMS algorithm to estimate the linear echo signal \(y\) and the error signal \(e\).

### TBNN post-filter

The TBNN post-filter takes \(d\), \(e\), and \(y\) after a short-time Fourier transform (STFT) as input. The complex-valued input spectra are compressed by a factor of 0.5, stacked by real and imaginary parts, and divided into wide-band signal \(I_{\text{WB}}\) and high-band signal \(I_{\text{HB}}\). Considering the complexity of direct full-band modeling and the high-band has less structural information, we use a two-step band-split approach to model full-band signals. The wide-band post-filter (WBPF) first uses spectral mapping to estimate the wide-band output \(\hat{S}_{\text{WB}}\). Then the high-band post-filter (HBPF) uses \(I_{\text{HB}}\) and priori information \(\hat{S}_{\text{WB}}\) to predict the complex-valued mask \(M_{\text{HB}}\), which is multiplied by \(I_{\text{HB}}\) to obtain the high-band output \(\hat{S}_{\text{HB}}\).

The backbone of the WBPF module is based on our previous gated convolutional recurrent network (GCRN) [2]. But differently, the encoder consists of 5 U\({}^{2}\)-Encoder layers as shown in Fig. 1(b). U\({}^{2}\)-Net [3] is a two-level nested U-structure designed to capture more contextual information from different scales (through Residual U-blocks) without significantly increasing the computational cost. Specifically, each U\({}^{2}\)-Encoder layer is composed of a gated convolution (GConv) layer, a batch-norm (BN) layer, a pReLU and an UNet-block. The number of input and output channels remains the same. The FTLSTM layer is served as a bottleneck for temporal modeling, as defined in [4]. The decoder consists of 5 decoder blocks as shown in Fig. 1(c), where TrConv refers to the Transpose-Conv2d layer, and 0 refers to multiply. The skip connection (implemented with 1x1 convolution) is applied between the encoder and decoder. We also add a voice activity detection (VAD) module to avoid over-suppression of near-end speech [2].

The HBBPF module uses three 2D-Conv modules to extract high-band features. Each 2D-Conv module consists of a 2D convolution (Conv2d) layer, an ELU layer, a BN layer and a dropout layer with a 0.25 dropout rate [5]. The wide-band output serves as prior information and it is concatenated with the high-band features after feature dimension alignment. Then we use the GRU layer and Conv2d layer to predict the real/imaginary mask, which is applied to the high-band input to obtain the high-band output.