Room Impulse Response estimation in Noisy Environments using Music as an Excitation Signal

Sammanfattning

This thesis investigates non-intrusive Room Impulse Response (RIR) estimation using music as an excitation signal in an acoustic environment with background noise present. The work studies the structure of room impulse responses, sparse background noise, babble noise, and music excitation signals in the context of RIR estimation. Building on the recently proposed AnyRIR estimator, which explored similar structures, weighted estimation methods incorporating background noise covariance are investigated under different background noise conditions. Robustness to sparse background disturbances is achieved through the Huber objective function formulation, reducing sensitivity to sporadic interference and resulting in high restored speech intelligibility. In babble noise conditions, incorporating background noise covariance matrix as weight within the Huber loss function increases accuracy of the estimated RIR. However, this did not lead to substantial improvements in restored speech intelligibility, suggesting that intelligibility is limited more by remaining structured babble interference than by RIR estimation accuracy itself. Results show that covariance weighting alone with weighted least squares is insufficient for RIR estimation in the presence of babble noise, producing higher normalized mean squared error (NMSE) of the estimated RIR compared to covariance weighted Huber estimation.