Estimation of Room Acoustic Parameters: The ACE Challenge

被引:115
作者
Eaton, James [1 ]
Gaubitch, Nikolay D. [2 ,3 ]
Moore, Alastair H. [1 ]
Naylor, Patrick A. [1 ]
机构
[1] Imperial Coll London, Dept Elect & Elect Engn, London SW7 2AZ, England
[2] Delft Univ Technol, NL-2628 CD Delft, Netherlands
[3] Pindrop Secur, Atlanta, GA 30308 USA
基金
英国工程与自然科学研究理事会;
关键词
Acoustic signal processing; parameter estimation; reverberation; BLIND ESTIMATION; REVERBERANT;
D O I
10.1109/TASLP.2016.2577502
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
Reverberation time (T-60) and Direct-to-reverberant ratio (DRR) are important parameters which together can characterize sound captured by microphones in nonanechoic rooms. These parameters are important in speech processing applications such as speech recognition and dereverberation. The values of T-60 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 aimed 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-60 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.
引用
收藏
页码:1681 / 1693
页数:13
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