Noise-robust blind reverberation time estimation using noise-aware time–frequency masking

Kaitong Zheng, Chengshi Zheng, Jinqiu Sang, Yulong Zhang, Xiaodong Li

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

The reverberation time is one of the most important parameters used to characterize the acoustic property of an enclosure. In real-world scenarios, it is much more convenient to estimate the reverberation time blindly from recorded speech compared to the traditional acoustic measurement techniques using professional measurement instruments. However, the recorded speech is often corrupted by noise, which has a detrimental effect on the estimation accuracy of the reverberation time. To address this issue, this paper proposes a two-stage blind reverberation time estimation method based on noise-aware time–frequency masking. This proposed method has a good ability to distinguish the reverberation tails from the noise, thus improving the estimation accuracy of reverberation time in noisy scenarios. The simulated and real-world acoustic experimental results show that the proposed method significantly outperforms other methods in challenging scenarios.

Original languageEnglish
Article number110901
JournalMeasurement: Journal of the International Measurement Confederation
Volume192
DOIs
StatePublished - 31 Mar 2022
Externally publishedYes

Keywords

  • Blind reverberation time estimation
  • Deep neural networks
  • Ideal ratio masking
  • Low signal-to-noise-ratio scenarios

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