Extracting Room Reverberation Time from Speech Using Artificial Neural Networks

Cox TJ, Li F, Darlington P. Extracting Room Reverberation Time from Speech Using Artificial Neural Networks. J Audio Eng Soc. 2001;49(4):219-230. Available from: https://aes.org/publications/elibrary-page/?id=10197

@article{Cox2001_10197,
  author = {Cox, Trevor J. and Li, Francis and Darlington, Paul},
  title = {{Extracting Room Reverberation Time from Speech Using Artificial Neural Networks}},
  journal = {Journal of the Audio Engineering Society},
  volume = {49},
  number = {4},
  pages = {219--230},
  year = {2001},
  month = apr,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=10197}
}

TY  - JOUR
TI  - Extracting Room Reverberation Time from Speech Using Artificial Neural Networks
AU  - Cox, Trevor J.
AU  - Li, Francis
AU  - Darlington, Paul
T2  - Journal of the Audio Engineering Society
J2  - J. Audio Eng. Soc.
VL  - 49
IS  - 4
SP  - 219
EP  - 230
PY  - 2001
DA  - 2001/04/06
UR  - https://aes.org/publications/elibrary-page/?id=10197
PB  - Audio Engineering Society
LA  - en
AB  - A novel method to extract the reverberation time from reverberated speech utterances is presented. In this study, speech utterances are restricted to pronounced digits; uncontrolled discourse is not considered. The reverberation times considered are wide band, within the frequency range of speech utterances. A multilayer feed forward neural network is trained on speech examples with known reverberation times generated by a room simulator. The speech signals are preprocessed by calculating short-term rms values. A second decision-based neural network is added to improve the reliability of the predictions. In the retrieve phase, the trained neural networks extract room reverberation times from speech signals picked up in the rooms to an accuracy of 0.1 s. This provides an alternative to traditional measurement methods and facilitates the occupied measurement of room reverberation times.
ER  -