Rendering of Source Spread for Arbitrary Playback Setups Based on Spatial Covariance Matching

McCormack, Leo; Politis, Archontis; Pulkki, Ville

doi:10.1109/waspaa52581.2021.9632724

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…Overall, parametric processing has been a promising avenue for binaural reproduction from microphone-array recordings, as it has the potential to capture important spatial information through the modelling process. Further research may provide high-quality reproduction even with challenging environments that include multiple dynamic sources, spatially complex sources [158,159], reverberation and noise, and by employing compact arrays with only a few microphones. Improved methods for estimating information on individual sources and on reverberant components, as well as methods that incorporate early room reflections [160][161][162][163], may advance the parametric approach even further.…”

Section: Parametric 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: Parametric 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

“…( 13). However, if the system is configured for binaural playback, then theoretically, only two channels of decorrelated audio should be required to produce the appropriate interchannel relationships corresponding to a diffuse-field, as demonstrated recently in [86]. Therefore, to reduce the computational complexity, similar solutions could be explored for the proposed ambient rendering.…”

Section: Avenues For Future Workmentioning

confidence: 99%

Object-Based Six-Degrees-of-Freedom Rendering of Sound Scenes Captured with Multiple Ambisonic Receivers

McCormack¹,

Politis²,

McKenzie³

et al. 2022

J. Audio Eng. Soc.

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This article proposes a system for object-based six-degrees-of-freedom (6DoF) rendering of spatial sound scenes that are captured using a distributed arrangement of multiple Ambisonic receivers. The approach is based on first identifying and tracking the positions of sound sources within the scene, followed by the isolation of their signals through the use of beamformers. These sound objects are subsequently spatialized over the target playback setup, with respect to both the head orientation and position of the listener. The diffuse ambience of the scene is rendered separately by first spatially subtracting the source signals from the receivers located nearest to the listener position. The resultant residual Ambisonic signals are then spatialized, decorrelated, and summed together with suitable interpolation weights. The proposed system is evaluated through an in situ listening test conducted in 6DoF virtual reality, whereby real-world sound sources are compared with the auralization achieved through the proposed rendering method. The results of 15 participants suggest that in comparison to a linear interpolation-based alternative, the proposed object-based approach is perceived as being more realistic.

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Demystifying Spatial Audio Plugins: Comparative Insights into Functionality and Features

Rosero,

Abreu,

Grijalva

et al. 2023

2023 IEEE Seventh Ecuador Technical Chapters Meeting (ECTM)

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Rendering of Source Spread for Arbitrary Playback Setups Based on Spatial Covariance Matching

Cited by 5 publications

References 12 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

Object-Based Six-Degrees-of-Freedom Rendering of Sound Scenes Captured with Multiple Ambisonic Receivers

Demystifying Spatial Audio Plugins: Comparative Insights into Functionality and Features

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