In this paper, we propose a speech-model based method using the linear predictive (LP) residual of the speech signal and the maximum-likelihood (ML) estimator proposed in “Blind estimation of reverberation time,” (R. Ratnam , J. Acoust. Soc. Amer., 2004) to blindly estimate the reverberation time $({rm RT}_{60})$. The input speech is passed through a low order linear predictive coding (LPC) filter to obtain the LP residual signal. It is proven that the unbiased autocorrelation function of this LP residual has the required properties to be used as an input to the ML estimator. It is shown that this method can successfully estimate the reverberation time with less data than existing blind methods. Experiments show that the proposed method can produce better estimates of ${rm RT}_{60}$, even in highly reverberant rooms. This is because the entire input speech data is used in the estimation process. The proposed method is not sensitive to the type of input data (voiced, unvoiced), number of gaps, or window length. In addition, evaluation using white Gaussian noise and recorded babble noise shows that it can estimate ${rm RT}_{60}$ in the presence of (moderate) background noise.
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