Estimation of Room Acoustic Parameters: The ACE Challenge

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...

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Bibliographic Details
Published in:IEEE/ACM transactions on audio, speech, and language processing speech, and language processing, 2016-10, Vol.24 (10), p.1681-1693
Main Authors: Eaton, James, Gaubitch, Nikolay D., Moore, Alastair H., Naylor, Patrick A.
Format: Article
Language:English
Subjects:
Acoustic properties
Acoustic signal processing
Acoustics
Additive noise
Estimation
Mathematical analysis
Microphones
Natural language processing
Noise measurement
Parameter estimation
Parameters
reverberation
Reverberation time
Speech
Speech processing
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Summary: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.
ISSN:2329-9290
2329-9304
DOI:10.1109/TASLP.2016.2577502