Blind estimation of room acoustic parameters using kernel regression

Room acoustic parameters are key information for dereverberation or speech recognition. Usually, when one needs to assess the level of reverberation, only the reverberation time RT60 or a direct to reverberant sounds index Dτ is estimated. Yet, methods which blindly estimate the reverberation time from reverberant recorded speech do not always differentiate the RT60 from the Dτ to evaluate the level of reverberation. That is why we propose a method to jointly blindly estimate these parameters, from the signal energy decay rate distribution, by means of kernel regression. Evaluation is carried out with real and simulated room impulse responses to generate noise-free reverberant speech signals. The results show this new method outperforms baseline approaches in our evaluation.