Single-Microphone LP Residual Skewness-Based Inverse Filtering of the Room Impulse Response

Abstract-Reverberation Time (T60) 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 T60 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 T60 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.

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“…The DRR at the m th microphone in each microphone configuration was calculated using the method of [46] as…”

Section: H Obtaining Drr Estimates From the Airmentioning

confidence: 99%

Estimation of Room Acoustic Parameters: The ACE Challenge

Eaton

Gaubitch

Moore

et al. 2016

IEEE/ACM Trans. Audio Speech Lang. Process.

118

109

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“…The purely diagonal Gabor filters as shown in Figure 3, were found to result in the maximal sensitivity to the reverberation effect [10] and thus, are used here to construct the Gabor features for the (T60, DRR) estimator. Each log-mel-spectrogram is filtered with these 48 filters in the filterbank PSfrag replacements 25.0 15.7 9.9 6. that cover temporal modulations from 2.4 to 25 Hz and spectral modulations from -0.25 to 0.25 cycles/channel, respectively.…”

Section: Blind (T60 Drr) Estimatormentioning

confidence: 99%

“…This method is shown to be more reliable under all conditions than the standard method according to ISO3382 [24]. The ground truth DRR is estimated using the method of [25], where the direct path is determined by the ±8 ms around the maximum found using an equalization filter [18].…”

Section: Ace Challengementioning

confidence: 99%

Joint Estimation of Reverberation Time and Early-To-Late Reverberation Ratio From Single-Channel Speech Signals

Xiong

Goetze

Kollmeier

et al. 2019

IEEE/ACM Trans. Audio Speech Lang. Process.

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Blind estimation of acoustic room parameters such as the reverberation time T60 and the direct-to-reverberation ratio (DRR) is still a challenging task, especially in case of blind estimation from reverberant speech signals. In this work, a novel approach is proposed for joint estimation of T60 and DRR from wideband speech in noisy conditions. 2D Gabor filters arranged in a filterbank are exploited for extracting features, which are then used as input to a multi-layer perceptron (MLP). The MLP output neurons correspond to specific pairs of (T60, DRR) estimates; the output is integrated over time, and a simple decision rule results in our estimate. The approach is applied to single-microphone fullband speech signals provided by the Acoustic Characterization of Environments (ACE) Challenge. Our approach outperforms the baseline systems with median errors of close-to-zero and -1.5 dB for the T60 and DRR estimates, respectively, while the calculation of estimates is 5.8 times faster compared to the baseline.

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“…Most single-channel speech dereverberation techniques can be classified into inverse filtering [10], [11], nonlinear mapping [12], spectral enhancement [6], [13], [14] and probabilistic model-based methods [15]- [17]. Inverse filtering methods typically try to reconstruct the original signal by designing an inverse filter for the Room Impulse Response (RIR).…”

Section: Introductionmentioning

confidence: 99%

Single-Channel Online Enhancement of Speech Corrupted by Reverberation and Noise

Doire

Brookes

Naylor

et al. 2017

IEEE/ACM Trans. Audio Speech Lang. Process.

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This paper proposes an online single-channel speech enhancement method designed to improve the quality of speech degraded by reverberation and noise. Based on an autoregressive model for the reverberation power and on a hidden Markov model for clean speech production, a Bayesian filtering formulation of the problem is derived and online joint estimation of the acoustic parameters and mean speech, reverberation, and noise powers is obtained in mel-frequency bands. From these estimates, a realvalued spectral gain is derived and spectral enhancement is applied in the short-time Fourier transform (STFT) domain. The method yields state-of-the-art performance and greatly reduces the effects of reverberation and noise while improving speech quality and preserving speech intelligibility in challenging acoustic environments.

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Single-Microphone LP Residual Skewness-Based Inverse Filtering of the Room Impulse Response

Cited by 28 publications

References 22 publications

Estimation of Room Acoustic Parameters: The ACE Challenge

Estimation of Room Acoustic Parameters: The ACE Challenge

Joint Estimation of Reverberation Time and Early-To-Late Reverberation Ratio From Single-Channel Speech Signals

Single-Channel Online Enhancement of Speech Corrupted by Reverberation and Noise

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