Estimation of room acoustic parameters: the ACE challenge. Eaton, J., Gaubitch, N. D., Moore, A. H., & Naylor, P. A. IEEE_ACM_J_ASLP, 24(10):1681–1693, October, 2016. tex.owner= dje11doi abstract bibtex Reverberation Time (T$_{\textrm{t}}$extrm60) and Direct-to-Reverberant Ratio (DRR) are important parameters which together can characterize sound captured by microphones in non-anechoic rooms. These parameters are important in speech processing applications such as speech recognition and dereverberation. The values of T$_{\textrm{t}}$extrm60 and DRR can be estimated directly from the Acoustic Impulse Response (AIR) of the room. In practice, the AIR is not normally available, in which case these parameters must be estimated blindly from the observed speech in the microphone signal. The Acoustic Characterization of Environments (ACE) Challenge set out to determine the state-of-the-art in blind acoustic parameter estimation and also to stimulate research in this area. A summary of the ACE Challenge, and the corpus used in the challenge is presented together with an analysis of the results. Existing algorithms were submitted alongside novel contributions, the comparative results for which are presented in this paper. The challenge showed that T$_{\textrm{t}}$extrm60 estimation is a mature field where analytical approaches dominate whilst DRR estimation is a less mature field where machine learning approaches are currently more successful.
@article{Eaton2016,
title = {Estimation of room acoustic parameters: the {ACE} challenge},
volume = {24},
issn = {2329-9290},
doi = {10.1109/TASLP.2016.2577502},
abstract = {Reverberation Time (T$_{\textrm{t}}$extrm60) and Direct-to-Reverberant Ratio (DRR) are important parameters which together can characterize sound captured by microphones in non-anechoic rooms. These parameters are important in speech processing applications such as speech recognition and dereverberation. The values of T$_{\textrm{t}}$extrm60 and DRR can be estimated directly from the Acoustic Impulse Response (AIR) of the room. In practice, the AIR is not normally available, in which case these parameters must be estimated blindly from the observed speech in the microphone signal. The Acoustic Characterization of Environments (ACE) Challenge set out to determine the state-of-the-art in blind acoustic parameter estimation and also to stimulate research in this area. A summary of the ACE Challenge, and the corpus used in the challenge is presented together with an analysis of the results. Existing algorithms were submitted alongside novel contributions, the comparative results for which are presented in this paper. The challenge showed that T$_{\textrm{t}}$extrm60 estimation is a mature field where analytical approaches dominate whilst DRR estimation is a less mature field where machine learning approaches are currently more successful.},
number = {10},
journal = {IEEE\_ACM\_J\_ASLP},
author = {Eaton, James and Gaubitch, Nikolay D. and Moore, Alastair H. and Naylor, Patrick A.},
month = oct,
year = {2016},
note = {tex.owner= dje11},
pages = {1681--1693},
}
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