Transmissibility operators are time-domain operators that model the relationship between outputs of an underlying system. Since real measurements are corrupted by noise, identification of transmissibilities is an errors-in-variables identification problem. This paper applies a bias-compensated recursive least-squares algorithm to errors-in-variables identification of transmissibilities. Noncausal FIR models are used to approximate transmissibility operators. To investigate the accuracy of this approach, we consider data from an acoustic experiment. The goal is to identify the transmissibility operator that models the relationship between measurements from two microphones.