E-URES 2.0: Efficient User-Centric Residual-Echo Suppression with a Lightweight Neural Network

We recently introduced the Efficient User-centric Residual-Echo Suppression (E-URES) framework, which significantly reduces the floating-point operations per second (FLOPS) required during inference by 90% compared to the URES framework. The E-URES operates based on a user-operating point (UOP) defined by two key metrics: the residual echo suppression level (RESL) and the desired-speech maintained level (DSML) that the user anticipates from the output signal of a residual echo suppression (RES) system. In the first stage, an ensemble of 101 branches is employed, where each branch has two cascaded neural networks: a preliminary RES system with a design parameter, which varies between branches and balances the RESL and DSML of its RES systems' prediction, and a subsequent UOP estimator. In the second stage, a neural network uses available acoustic signals and the UOP to predict which three branches achieve the highest acoustic echo cancellation mean opinion score (AECMOS) within a specified UOP-error tolerance. Then, costly AECMOS calculations are performed only for these selected branches. Despite this efficiency mechanism, the E-URES can apply real-time inference only with dedicated and expensive hardware, limiting its wide adoption. Here, we present E-URES 2.0, which focuses on reducing the computational costs of E-URES in its first stage. A lightweight neural network preprocesses available acoustic signals and the UOP to track a subset of the 101 design parameters that their branches produce the most accurate UOP estimations in their outcomes. Only these branches are calculated during inference and continue to the AECMOS estimation stage. With 60 hours of data, we show that with a negligible performance drop on average, the E-URES 2.0 can reduce 87% of the branches and 61% of the FLOPS of the E-URES and can achieve real-time inference with standard, affordable hardware.