Blind Estimation of Room Acoustic Parameters and Speech Transmission Index using MTF-based CNNs

Duangpummet, Suradej; Karnjana, Jessada; Kongprawechnon, Waree; Unoki, Masashi

doi:10.23919/eusipco54536.2021.9616044

Cited by 3 publications

(1 citation statement)

References 8 publications

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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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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.

View full text Add to dashboard Cite

show abstract

Blind Reverberation Time Estimation in Dynamic Acoustic Conditions

Götz

Tuna

Walther

et al. 2022

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Online reverberation time and clarity estimation in dynamic acoustic conditions

Götz

Tuna

Walther

et al. 2023

The Journal of the Acoustical Society of America

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Previously proposed methods for estimating acoustic parameters from reverberant, noisy speech signals exhibit insufficient performance under changing acoustic conditions. A data-centric approach is proposed to overcome the limiting assumption of fixed source–receiver transmission paths. The obtained solution significantly enlarges the scope of potential applications for such estimators. The joint estimation of reverberation time RT60 and clarity index C50 in multiple frequency bands is studied with a focus on dynamic acoustic environments. Three different convolutional recurrent neural network architectures are considered to solve the tasks of single-band, multi-band, and multi-task parameter estimation. A comprehensive performance evaluation is provided that highlights the benefits of the proposed approach.

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Blind Estimation of Room Acoustic Parameters and Speech Transmission Index using MTF-based CNNs

Cited by 3 publications

References 8 publications

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

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

Blind Reverberation Time Estimation in Dynamic Acoustic Conditions

Online reverberation time and clarity estimation in dynamic acoustic conditions

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