@inproceedings{genovese2019blind,
author = {Genovese, Andrea and Gamper, Hannes and Pulkki, Ville and Raghuvanshi, Nikunj and Tashev, Ivan},
title = {Blind Room Volume Estimation from Single-channel Noisy Speech},
organization = {IEEE},
booktitle = {Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
year = {2019},
month = {May},
abstract = {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).},
url = {http://approjects.co.za/?big=en-us/research/publication/blind-room-volume-estimation-from-single-channel-noisy-speech/},
}