Abstract
A new framework is proposed in this paper to solve the reverberation time (RT) estimation problem in occupied rooms. In this framework, blind source separation (BSS) is combined with an adaptive noise canceller (ANC) to remove the noise from the passively received reverberant speech signal. A polyfit preprocessing step is then used to extract the free decay segments of the speech signal. RT is extracted from these segments with a maximum-likelihood (ML) based method. An easy, fast and consistent method to calculate the RT via the ML estimation method is also described. This framework provides a novel method for blind RT estimation with robustness to ambient noises within an occupied room and extends the ML method for RT estimation from noise-free cases to more realistic situations. Simulation results show that the proposed framework can provide a good estimation of RT in simulated low RT occupied rooms.