Evaluating the Non-Intrusive Room Acoustics Algorithm with the ACE Challenge
Sound
2015-10-16 v1
Abstract
We present a single channel data driven method for non-intrusive estimation of full-band reverberation time and full-band direct-to-reverberant ratio. The method extracts a number of features from reverberant speech and builds a model using a recurrent neural network to estimate the reverberant acoustic parameters. We explore three configurations by including different data and also by combining the recurrent neural network estimates using a support vector machine. Our best method to estimate DRR provides a Root Mean Square Deviation (RMSD) of 3.84 dB and a RMSD of 43.19 % for T60 estimation.
Cite
@article{arxiv.1510.04616,
title = {Evaluating the Non-Intrusive Room Acoustics Algorithm with the ACE Challenge},
author = {Pablo Peso Parada and Dushyant Sharma and Toon van Waterschoot and Patrick A. Naylor},
journal= {arXiv preprint arXiv:1510.04616},
year = {2015}
}
Comments
In Proceedings of the ACE Challenge Workshop - a satellite event of IEEE-WASPAA 2015 (arXiv:1510.00383)