Blind Estimation of Reverberation Time using Binaural Complex Ideal Ratio Mask

Accurate estimation of reverberation time T_60 proved to have a positive effect on the automatic speech recognition (ASR) used in the voice-controlled devices and the reconstruction of the acoustic field. Recently, researchers have proposed some algorithms to estimate T_60. However, few of them directly use the spatial information about the acoustic environment contained in the speech for accurate T_60 estimation. We propose a deep learning approach as a regression problem to use binaural reverberant speech generated by the clean speech convolved with simulated Room Impulse Response (RIR) to estimate T_60. Adaptive cIRM estimator firstly estimates the complex Ideal Ratio Mask (cIRM), which is strongly correlated with T_60, and then a CNN-based T_60 estimator is used to estimate T_60 with cIRM. The experimental results show that our proposed approach outperforms the state-of-the-art method of T_60 estimation.