Blind Room Volume Estimation from Single-channel Noisy Speech

Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) |

Organized by IEEE

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Recent work on acoustic parameter estimation indicates that geometric room volume can be useful for modeling the character of an acoustic environment. However, estimating volume from audio signals remains a challenging problem. Here we propose using a convolutional neural network model to estimate the room volume blindly from reverberant single-channel speech signals in the presence of noise. The model is shown to produce estimates within approximately a factor of two to the true value, for rooms ranging in size from small offices to large concert halls.

Figure: Confusion matrices of the training set (left), test set (center), and the ACE corpus (right).