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@article{Papayiannis:2020:10.1109/TASLP.2020.3033628,
author = {Papayiannis, C and Evers, C and Naylor, P},
doi = {10.1109/TASLP.2020.3033628},
journal = {IEEE Transactions on Audio, Speech and Language Processing},
pages = {3010--3017},
title = {End-to-end classification of reverberant rooms using DNNs},
url = {http://dx.doi.org/10.1109/TASLP.2020.3033628},
volume = {28},
year = {2020}
}
TY - JOUR
AB - Reverberation is present in our workplaces, ourhomes, concert halls and theatres. This paper investigates howdeep learning can use the effect of reverberation on speechto classify a recording in terms of the room in which it wasrecorded. Existing approaches in the literature rely on domainexpertise to manually select acoustic parameters as inputs toclassifiers. Estimation of these parameters from reverberantspeech is adversely affected by estimation errors, impacting theclassification accuracy. In order to overcome the limitations ofpreviously proposed methods, this paper shows how DNNs canperform the classification by operating directly on reverberantspeech spectra and a CRNN with an attention-mechanism isproposed for the task. The relationship is investigated betweenthe reverberant speech representations learned by the DNNs andacoustic parameters. For evaluation, AIRs are used from theACE-challenge dataset that were measured in 7 real rooms. Theclassification accuracy of the CRNN classifier in the experimentsis 78% when using 5 hours of training data and 90% when using10 hours.
AU - Papayiannis,C
AU - Evers,C
AU - Naylor,P
DO - 10.1109/TASLP.2020.3033628
EP - 3017
PY - 2020///
SN - 1558-7916
SP - 3010
TI - End-to-end classification of reverberant rooms using DNNs
T2 - IEEE Transactions on Audio, Speech and Language Processing
UR - http://dx.doi.org/10.1109/TASLP.2020.3033628
UR - https://ieeexplore.ieee.org/document/9239871
UR - http://hdl.handle.net/10044/1/83971
VL - 28
ER -
Speech and Audio Processing Lab
CSP Group, EEE Department
Imperial College London
Exhibition Road, London, SW7 2AZ, United Kingdom