Blind Reverberation Time Estimation in Dynamic Acoustic Conditions

Götz, Philipp; Tuna, Çağdaş; Walther, Andreas; Habets, Emanuël A. P.

doi:10.1109/icassp43922.2022.9746457

Cited by 9 publications

(5 citation statements)

References 18 publications

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“…Proposed blind estimation methods for STI and RAPs are based on either analytical or learning-based approaches [4], [19], [20], [21], [22], [23], [24], [29], [30], [31], [32], [33], [34], [35], [36], [37]. Ones following the analytical approach achieve blind estimation by creating an explicit mapping between the observed reverberant signals and the desired parameters.…”

Section: Related Workmentioning

confidence: 99%

“…However, the current methods can estimate only a single parameter [19], [20], [21], [22], [24], [29], [30], [31], [32], [33], [36], [40]. Although the MTF-based CNN method can estimate multiple parameters, it is limited to the training data used to derive the model, the same as the other learning-based methods [4], [32], [33], [34], [35], [36], [37].…”

Section: Related Workmentioning

confidence: 99%

“…Since (22) filters out the noise, we assume that the TPE of a clean signal is affected only by reverberation. Thus, the TPE of an observed reverberant signal e 2 y (t) can be determined as:…”

Section: Proofmentioning

confidence: 99%

“…With the recent trend of machine learning in the field of signal processing, artificial neural networks have been widely applied to seek out the implicit mapping between the observed reverberant signal and the desired RAPs. Götz et al combined the auditory-motivated Gammatone filterbank and convolutional neural network (CNN) to blindly estimate the reverberation time in dynamic acoustic conditions [22]. Zheng et al proposed a robust estimation method for the reverberation time under the noisy conditions using gated convolutional recurrent network [23].…”

Section: Introductionmentioning

confidence: 99%

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Blind Estimation of Speech Transmission Index and Room Acoustic Parameters by Using Extended Model of Room Impulse Response Derived From Speech Signals

2023

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The speech transmission index (STI) and room acoustic parameters (RAPs) are essential metrics for assessing speech quality and predicting listening difficulty in a sound field. Although STI and important RAPs, such as reverberation time and clarity, can be derived from the room impulse response (RIR), measuring the RIR in regularly occupied spaces is difficult. Hence, simultaneous blind estimation of STI and RAPs is an imperative challenge issue that must be addressed. However, most existing methods provide only a single parameter and require a massive dataset for model training. A deterministic method is presented for blindly estimating STI and five RAPs using a stochastic RIR model that approximates an unknown RIR. An algorithm is formulated that uses the temporal power envelope of a reverberant speech signal to determine the optimal parameters of the RIR model. A mathematical model of reverberation and dereverabation process was proposed based on the temporal power envelope of the signals. This model maps the parameters of the RIR model to the observed reverberant signal. The estimated RIR can then be synthesized using the optimal parameters to estimate the STI and RAPs. A simulation was conducted to evaluate the simultaneous estimation of STI and five essential RAPs from observed reverberant speech signals, in comparison to the best existing previous work. The root-mean-square error (RMSE) and Pearson correlation coefficient between the estimated and measured values were used as evaluation metrics. In terms of STI, the proposed method achieves the accuracy with an RMSE of 0.037. With regard to the reverberation time and other RAPs, the accuracy remains consistent with the previous works. The results show that the proposed method can effectively estimate STI and RAPs simultaneously without any training.INDEX TERMS Room impulse response, modulation transfer function, reverberation, speech transmission index, room acoustic parameters.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Proofmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Blind Estimation of Speech Transmission Index and Room Acoustic Parameters by Using Extended Model of Room Impulse Response Derived From Speech Signals

2023

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“…Grumiaux proposed the use of the time-domain velocity vector as an input feature for a deep neural network (DNN) to count and localize multiple speakers in Ambisonics signals [167]. A related problem is the blind estimation of room acoustic parameters from audio recordings [168], including the estimation of reverberation time and the early-to-late reverberation ratio [169][170][171][172][173].…”

Section: Machine-and Deep-learning Based Processingmentioning

confidence: 99%

Spatial audio signal processing for binaural reproduction of recorded acoustic scenes – review and challenges

Rafaely

Tourbabin²,

Habets³

et al. 2022

Acta Acust.

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Spatial audio has been studied for several decades, but has seen much renewed interest recently due to advances in both software and hardware for capture and playback, and the emergence of applications such as virtual reality and augmented reality. This renewed interest has led to the investment of increasing efforts in developing signal processing algorithms for spatial audio, both for capture and for playback. In particular, due to the popularity of headphones and earphones, many spatial audio signal processing methods have dealt with binaural reproduction based on headphone listening. Among these new developments, processing spatial audio signals recorded in real environments using microphone arrays plays an important role. Following this emerging activity, this paper aims to provide a scientific review of recent developments and an outlook for future challenges. This review also proposes a generalized framework for describing spatial audio signal processing for the binaural reproduction of recorded sound. This framework helps to understand the collective progress of the research community, and to identify gaps for future research. It is composed of five main blocks, namely: the acoustic scene, recording, processing, reproduction, and perception and evaluation. First, each block is briefly presented, and then, a comprehensive review of the processing block is provided. This includes topics from simple binaural recording to Ambisonics and perceptually motivated approaches, which focus on careful array configuration and design. Beamforming and parametric-based processing afford more flexible designs and shift the focus to processing and modeling of the sound field. Then, emerging machine- and deep-learning approaches, which take a further step towards flexibility in design, are described. Finally, specific methods for signal transformations such as rotation, translation and enhancement, enabling additional flexibility in reproduction and improvement in the quality of the binaural signal, are presented. The review concludes by highlighting directions for future research.

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Attention Is All You Need For Blind Room Volume Estimation

Wang,

Jia,

et al. 2024

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Blind Reverberation Time Estimation in Dynamic Acoustic Conditions

Cited by 9 publications

References 18 publications

Blind Estimation of Speech Transmission Index and Room Acoustic Parameters by Using Extended Model of Room Impulse Response Derived From Speech Signals

Blind Estimation of Speech Transmission Index and Room Acoustic Parameters by Using Extended Model of Room Impulse Response Derived From Speech Signals

Spatial audio signal processing for binaural reproduction of recorded acoustic scenes – review and challenges

Attention Is All You Need For Blind Room Volume Estimation

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