Audio Signal Processing for Telepresence Based on Wearable Array in Noisy and Dynamic Scenes

Beit-On, Hanan; Lugasi, Moti; Madmoni, Lior; Menon, Anjali; Kumar, Anurag; Donley, Jacob; Tourbabin, Vladimir; Rafaely, Boaz

doi:10.1109/icassp43922.2022.9747583

Cited by 7 publications

(4 citation statements)

References 15 publications

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“…In more recent studies, the beamforming approach developed in previous work was applied to arrays of arbitrary configuration, such as arrays mounted on helmets [142], or glasses [143], linear arrays [43,144], and wall mounted planar arrays [145]. These recent studies extended previous work which was mostly developed for Ambisonics signals.…”

Section: Beamforming-based Processingmentioning

confidence: 89%

“…These recent studies extended previous work which was mostly developed for Ambisonics signals. Design methods were further developed by proposing a framework for selecting the number of beamforming directions [145], by direct matching of microphone signals to binaural signals [44,143], and by designing virtual artificial heads [43,144]. The last may require efficient representations of HRTF, e.g., [146].…”

Section: Beamforming-based Processingmentioning

confidence: 99%

“…This process leads to a reconstruction of the desired Ambisonics signal with the full spatial information, while providing enhancement through the contribution of the beamforming [231]. Recently, this approach was also investigated for a wearable microphone array [143,158]. An alternative approach, also aiming to achieve significant enhancement while preserving spatial information, employed masking in the time-frequency domain, applied directly to the Ambisonics signals or to the same signals spatially transformed by beamforming [232].…”

Section: Signal Enhancementmentioning

confidence: 99%

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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: Beamforming-based Processingmentioning

confidence: 89%

Section: Beamforming-based Processingmentioning

confidence: 99%

Section: Signal Enhancementmentioning

confidence: 99%

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

Rafaely

Tourbabin²,

Habets³

et al. 2022

Acta Acust.

Self Cite

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“…There are two design principles for a BAT system that can be found in the literature. A three-stage Localization-Beamforming-HRTF Filtering (LBH) approach was suggested [24]. This approach can provide excellent binaural output with accurate directional perception at the expense of audio ambience.…”

Section: Introductionmentioning

confidence: 99%

Model-Matching Principle Applied to the Design of an Array-Based All-Neural Binaural Rendering System for Audio Telepresence

Hsu

Bai

2023

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Audio Telepresence (AT) aims to create an immersive experience of the audio scene at the far end for the user(s) at the near end. The application of AT could encompass scenarios with varying degrees of emphasis on signal enhancement and ambience preservation. It is desirable for an AT system to be scalable between these two extremes. To this end, we propose an array-based Binaural AT (BAT) system using the DeepFilterNet as the backbone to convert the array microphone signals into the Head-Related Transfer Function (HRTF)-filtered signals, with a tunable weighting between signal enhancement and ambience preservation. An array configuration-independent Spatial COherence REpresentation (SCORE) feature is proposed for the model training so that the network remains robust to different array geometries and sensor counts. magnitude-weighted Interaural Phase Difference error (mw-IPDe), magnitude-weighted Interaural Level Difference error (mw-ILDe), and modified Scale-Invariant Signal-to-Distortion Ratio (mSI-SDR) are defined as performance metrics for objective evaluation. Subjective listening tests were also performed to validate the proposed BAT system. The results have shown that the proposed BAT system can achieve superior telepresence performance with the desired balance between signal enhancement and ambience preservation, even when the array configurations are unseen in the training phase.

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ReVISE: Self-Supervised Speech Resynthesis with Visual Input for Universal and Generalized Speech Regeneration

Hsu,

Remez,

Shi

et al. 2023

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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Audio Signal Processing for Telepresence Based on Wearable Array in Noisy and Dynamic Scenes

Cited by 7 publications

References 15 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

Model-Matching Principle Applied to the Design of an Array-Based All-Neural Binaural Rendering System for Audio Telepresence

ReVISE: Self-Supervised Speech Resynthesis with Visual Input for Universal and Generalized Speech Regeneration

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