Speaker
Description
Adaptive local active noise control (ANC) requires accurate estimates of the sound pressure at the point of cancellation. These are often obtained using nearby microphones via virtual sensing methods such as the remote microphone technique (RMT). Obs-TasNet was recently proposed as a neural network–based method for online estimation of RMT observation filter coefficients. In this work, we evaluate Obs-TasNet's performance in synthesized reverberant environments with stochastic as well as nonstationary sounds sampled from the FSD50K dataset. Overall, Obs-TasNet exhibits only minor degradation of estimation error when applied to nonstationary sounds compared to stochastic noise sources. In reverberant environments, performance declines toward higher frequencies approaching the array's aliasing frequency. Nevertheless, Obs-TasNet consistently outperforms an inverse-distance-weighting baseline. These results indicate that Obs-TasNet is robust across source types and can handle acoustic conditions relevant to practical deployment.