@inproceedings{gamper2018blind,
author = {Gamper, Hannes and Tashev, Ivan},
title = {Blind reverberation time estimation using a convolutional neural network},
booktitle = {Proc. International Workshop on Acoustic Signal Enhancement (IWAENC)},
year = {2018},
month = {September},
abstract = {The reverberation time of an acoustic environment is a useful parameter for applications including source localisation, speech recognition and mixed reality. However, estimating the reverberation time blindly and on the fly remains a challenge. Here we propose formulating the estimation as a regression problem and using a convolutional neural network (CNN) to estimate the reverberation time directly from a four second long single-channel recording of reverberant speech in noise. Evaluation on the ACE Challenge data corpus suggests that the proposed method is computationally efficient and outperforms state-of-the-art methods.},
publisher = {IEEE},
url = {http://approjects.co.za/?big=en-us/research/publication/blind-reverberation-time-estimation-using-a-convolutional-neural-network/},
pages = {1-5},
note = {Nominated for best paper award},
}