On Improving the Performance of a Speech Model-Based Blind Reverberation Time Estimation in Noisy Environments

This paper proposes a method of improving the performance of blind reverberation time (RT) estimation in noisy environments. RT estimation is conducted using a maximum likelihood (ML) method based on the autocorrelation function of the linear predictive residual signal. To reduce the effect of environmental noise, a noise reduction technique is applied to the noisy speech signal. In addition, a frequency coefficient selection is performed to eliminate signal components with low signal-to-noise ratio (SNR). Experimental results confirm that the proposed method improves the accuracy of RT measures, particularly when the speech signal is corrupted by a colored noise with a narrow bandwidth.