## 1 Introduction  
The standard acoustic echo cancellation (AEC) setup is used by communication terminals comprised of one loudspeaker and one microphone. The AEC process employs an adaptive filter to estimate an unknown impulse response, which generates undesired replicas of the loudspeaker signal [1; 2; 3]. The goal is to eliminate the unknown system's output (or the echo) contribution from the mix of signals captured by the microphone, which is situated in the same room [1; 4; 5; 6; 7]. The corresponding unknown system identification configuration uses as input the loudspeaker signal, with the microphone's input as a reference. From the microphone contributions, an echo estimate is subtracted, and the resulting signal is sent to the interlocutor, ideally without any unwanted acoustic replicas. When the communication terminal consists of \(N\) loudspeakers with \(M\) microphones, the number of possible echo paths is given by the value \(N\times M\)[4; 8; 9; 10]. The stereophonic communication terminal represents the minimal configuration which can provide the impression of audio directionality, having \(N=M=2\). In order to perform the stereophonic AEC (SAEC), we must take into consideration that each loudspeaker-to-microphone pair can generate unwanted acoustic replicas. Correspondingly, two input (loudspeaker) signals with two microphone signals, are used in four AEC-type system identification setups in order to estimate four corresponding unknown impulse responses [4; 8; 11; 12; 13]. Additionally, compared to the single channel scenarios, the SAEC process is also hindered by the existing correlation between the two acoustic channels, which might not generate unique solutions when using adaptive algorithms [8; 9; 14; 15; 16; 17]. In the recent literature, the solution to the problem represented by the coherence between the acoustic channels has been approachedby employing a pre-distortion block, which mitigates the correlation with some cost in terms of signal quality [4; 5; 9; 18]. In [5; 18], the widely linear (WL) model was introduced in order to improve the handling of the SAEC framework when employing several types of adaptive filters. It groups the four adaptive filters and multiple real valued variables into a single adaptive filter and fewer complex valued variables (CRVs). Moreover, the WL approach also opened the way for other various developments proposed for SAEC systems, such as improved behaviour when working in low signal-to-noise ratio (SNR) conditions [19]. Although considerable effort was invested in enhancing robustness features, the SAEC setup interpreted using the WL framework leaves more room for applying developments which has proved to be successful for other configurations employing adaptive systems. Among the essential performance indicators for any adaptive algorithm in AEC/SAEC scenarios (i.e., _system identification_ setups) [1; 4; 20], the tracking speed represents the capacity of following any changes that might occur in the unknown echo paths. The current adaptive systems in the industry employ algorithms from the least-mean-square (LMS) family for most practical applications [1]. The LMS methods have the advantage of delivering satisfactory performance with a relatively limited arithmetic effort with respect to other potential solutions. However, they provide poor results when working with highly correlated input signals (such as speech) in terms of tracking speed and accuracy at steady state (i.e., when convergence is achieved). Consequently, due to their capabilities of reducing the correlation of input signals and generating superior tracking performances, the recursive least-squares (RLS) family of algorithms is the subject to considerable research. They are also known for their excessively costly arithmetic workloads, which make them prohibitive for practical applications. However, a successful attempt at mitigating this problem is the combination between the exponentially weighted RLS and the dichotomous coordinate descent (DCD) iterations [5; 21; 22].