RNN-Based Step-Size Estimation for the RLS Algorithm with Application to Acoustic Echo Cancellation

In this paper, we propose an recurrent neural network (RNN) based step-size estimation for the recursive least squares (RLS) algorithm with application to acoustic echo cancellation (AEC). RLS-based AEC (as compared to the least mean square (LMS) based) has a better convergence rate and less distortion, which is an important advantage in acoustic speech recognition (ASR) usages. The optimal step size of the RLS, as derived in the literature, is impractical due to its dependence on the power of the desired speech. An RNN model is presented that learns the relationship between the reference signal and the microphone signal to the optimal step size. At inference mode, the trained RNN produces the step size in real-time, which is then fed to the RLS algorithm. For evaluation, we used two hours of recordings from the AEC-challenge and Wav2vec2 databases, and compared the proposed technique and other competing methods. Experiments show that the proposed technique has advantages in terms of ASR performance and other classic measurements.