## 1 Introduction  
Acoustic echo cancellation is a long-standing problem in real-life telecommunication scenarios where a near-end speaker communicates with a far-end speaker. A loudspeaker plays the far-end signal, and a microphone captures the echo of the loudspeaker signal, and the near-end signal and background noise [1]. Traditional acoustic echo cancellers (AECs) employ linear adaptive filters [2]. Linear AECs commonly use the least mean squares (LMS) algorithm [3, 4] and its normalized version, the normalized LMS (NLMS) [5, 6]. The improvement introduced by the normalization is that the step size can be set independently of the reference signal's power [7]. Variants of the LMS and NLMS algorithms are the sign-error LMS (SLMS), and normalized SLMS (NSLMS) algorithms [8]. In contrast to the NLMS, the NSLMS adjusts the weight for each filter tap, based on the polarity (sign) of the error signal. Several studies have shown the advantages of the NSLMS over the NLMS. For example, [PERSON] and [PERSON] [9] used the NSLMS adaptive filter to cancel geomagnetic background noise in magnetic anomaly detection systems and demonstrated its superiority over the NLMS. [PERSON] et al. [10] utilized the NSLMS adaptive filter to perform speech enhancement in noisy magnetic resonance imaging (MRI) environments. According to their experiments, the NSLMS achieved faster convergence than the NLMS, and residual noise produced by the NSLMS had characteristics of white noise. In contrast, residual noise produced by the NLMS was more structured. The linear AECs lack the ability to cancel the nonlinear components of the echo. Therefore, further suppression of the residual echo is required, and a residual echo suppressor (RES) is typically employed. While traditional residual echo suppression relies on filter-based techniques [11; 12], recent advances in deep learning have shifted the focus toward neural network-based approaches [13; 14; 15; 16]. Under challenging real-life conditions, for example, low signal-to-echo ratios (SERs) and changing acoustic echo paths, the performance of the linear AEC preceding the RES model has a significant impact on the overall performance. Hence, it may be beneficial to investigate the AECs in conjunction with deep-learning models for residual echo suppression. The output of a linear AEC is expected to contain a distorted weaker version of the echo signal, while keeping the near-end signal almost distortionless. Therefore, denoising the estimated near-end signal with a designated speech denoiser might suppress the residual echo, while eliminating other noises. Research on deep-learning-based speech enhancement algorithms has seen significant progress over the last few years, with many models exhibiting excellent performances [17; 18; 19]. For a speech denoiser to achieve good performance as an RES, the AEC must produce residual echo that closely resembles noise, rather than human speech. In this paper, two aspects of residual echo suppression were investigated: the impact of the preceding linear AEC on the performance of the residual echo suppression deep-learning model and the utilization of a pre-trained speech denoiser as an alternative to an RES. In addition, an echo suppression system, that employs NSLMS to perform linear acoustic echo cancellation and a deep complex convolutional recurrent network (DCCRN) [18] to achieve residual echo suppression, is proposed. The performances of the NSLMS and the commonly-used NLMS algorithms were compared, and the utilization of a speech denoiser to the output of the linear AEC to suppress the residual echo and additional noises was evaluated. The results showed that the performances of systems using NSLMS were superior to those using NLMS in all settings. This suggested that NSLMS was better suited for acoustic echo cancellation and residual echo suppression tasks, emphasizing the importance of choosing the right linear AEC.