Blind Room Parameter Estimation Using Multiple-Multichannel Speech Recordings

Srivastava, Prerak; Deleforge, Antoine

doi:10.48550/arxiv.2107.13832

Cited by 1 publication

(1 citation statement)

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“…In 2015, the ACE challenge workshop was held to evaluate the blind methods in estimating T 60 with the recorded noisy reverberant speech [13]. With the rapid development of deep learning, several deep learning-based methods have been proposed after the ACE workshop and some of them have surpassed traditional methods on estimation accuracy [14,15,16]. Most of these methods have shown satisfactory improvements in noise-free and high signal-tonoise-ratio (SNR) scenarios while the T 60 estimation in low SNR scenarios such as 0 dB are still challenging.…”

Section: Introductionmentioning

confidence: 99%

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

Zheng¹,

Zheng²,

Sang³

et al. 2021

Preprint

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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.

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