Pith. sign in

REVIEW 3 major objections 5 minor 40 references

Compression of Higher Order Ambisonics with Multichannel RVQGAN

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A 16-channel neural codec compresses third-order Ambisonics ambience to 16 kbps with 'good' rated spatial quality, beating Opus at ten times the bitrate.

desk verdict A modest but honest multichannel RVQGAN extension for 16-channel Ambisonics, with a real evaluation weakness: the MUSHRA test never leaves the training scenes. read the letter →

arxiv 2411.12008 v3 pith:6BHJZYAS submitted 2024-11-18 cs.SD cs.LGcs.MMeess.AS

Toni Hirvonen , Mahmoud Namazi This is my paper · ORCID
classification cs.SDcs.LGcs.MMeess.AS
keywords HigherOrderAmbisonicsneuralaudiocodingRVQGANmultichannelcompressioncovariancelossMUSHRAlisteningtesttransferlearningimmersive
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that a neural audio codec can compress 16-channel third-order Ambisonics, the spherical-harmonics representation used for capturing spatial sound, to 16 kbps without collapsing the spatial image. The authors extend the RVQGAN neural codec to accept all 16 channels by widening only the first and last convolutional layers, add a loss term that penalizes mismatches in the normalized inter-channel covariance matrix, and initialize the multichannel model from a pretrained single-channel model. In a MUSHRA listening test with 7.1.4 loudspeaker playback, listeners rated the 16 kbps neural codec as 'good' and above Opus coding the same material at 160 kbps. If the result holds beyond the tested ambient scenes, it would make data-driven compression a practical option for scene-based immersive audio at a tenth of the bitrate of a conventional codec.

What carries the argument

The central object is a multichannel extension of the RVQGAN neural codec: the first and last convolutional layers are widened from one channel to 16, while the shared bottleneck stays unchanged so the bitrate remains 16 kbps across all channels. The spatial-perception mechanism is the covariance loss, defined as the L1 distance between the normalized channel-wise covariance matrices of the original and reconstructed time-domain signals, $$L_{\text{cov}} = \frac{1}{2}\sum_{i=0}^{n}\sum_{j=0}^{n} \left\| \frac{C_{ij}}{\sqrt{C_{ii}C_{jj}}} - \frac{\hat{C}_{ij}}{\sqrt{\hat{C}_{ii}\hat{C}_{jj}}} \right\|,$$ where each entry is the Pearson correlation between channels $i$ and $j$. The paper also relies on transfer learning, copying the pretrained single-channel weights into all 16 input and output channels before fine-tuning, which the authors show speeds convergence and lowers validation loss compared with random initialization.

What would settle it

Retrain the same model with a leave-one-scene-out split, so the MUSHRA stimuli come from a scene entirely absent during training, and compare to Opus at 160 kbps; if the proposed codec no longer reaches 'good' or falls below Opus on unseen scenes, the suitability claim would be restricted to familiar environments.

Watch

Extended reading notes

Core claim

The central claim is that a multichannel extension of RVQGAN, achieved by widening only the input and output convolutional layers to 16 channels and adding an inter-channel covariance loss, compresses third-order Ambisonics ambience to 16 kbps while preserving spatial impression. In a MUSHRA listening test over a 7.1.4 loudspeaker layout, the proposed 16 kbps codec reached 'good' quality on the MUSHRA scale and outperformed Opus coding the same HOA content at 160 kbps, a tenfold higher bitrate. The authors present this as evidence that the method is suitable for coding scene-based, 16-channel Ambisonics content, while noting that informal listening on musical material outside the training database suggests the model does not generalize to all content.

Load-bearing premise

The load-bearing premise is that the eight ambient scenes used for training and the held-out clips used for listening are similar enough that a codec which performs well on held-out clips of those scenes will also perform well on new acoustic environments—the test never presents a scene the model has not already heard.

Editorial extensions

If this is right

  • A 16-channel third-order Ambisonics stream can be transported at 16 kbps with 'good' rated quality in 7.1.4 playback, matching the bitrate of a mono neural codec while carrying spatial content.
  • The tenfold bitrate advantage over Opus at 160 kbps, if replicated on other content, would make neural methods attractive for streaming scene-based immersive audio.
  • Transfer learning from a pretrained single-channel model reduces training time and improves the final reconstruction loss relative to training the multichannel model from scratch.
  • The method as presented is limited to ambient scene-based material; musical, cinematic, or mixed presentations may require higher bitrates or additional training data.
  • The architecture is channel-count-agnostic, so the same extension could in principle be applied to other multichannel formats without redesigning the bottleneck.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the covariance loss is the main driver of spatial-quality gain, a frequency-dependent version of the same normalized-covariance penalty would be a natural next test, since broadband correlation cannot capture frequency-selective envelopment cues.
  • Because the evaluation splits held-out clips from the same eight scenes used in training, the reported margin over Opus is likely an upper bound for truly unseen ambiences; a leave-one-scene-out MUSHRA would quantify the drop.
  • The channel-count-agnostic design suggests the same codec could be trained for 5.1, 7.1.4 loudspeaker signals, or binaural renders whenever enough paired data exists, not just for 16-channel scene-based HOA.
  • A practical extension left implicit is a rate-adaptive variant: the fixed 16 kbps bottleneck is the main obstacle to deploying the codec where available bandwidth varies.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper proposes a multichannel extension of RVQGAN for coding 16-channel third-order Ambisonics audio. The architecture changes are limited to the input and output layers of the generator and discriminators, a normalized interchannel covariance loss is added for spatial fidelity, and transfer learning from a pretrained single-channel model is used. The system is trained on the EigenScape database at a 7/8-per-scene train/test split and evaluated in a MUSHRA listening test with 7.1.4 loudspeaker playback against a 160-kbps Opus Ambisonics coder, a low anchor, and a hidden reference. The authors report "good" quality at 16 kbps and better mean scores than Opus at 160 kbps, with the stated caveat that informal listening outside EigenScape indicated limited generality.

Significance. If the reported results hold, the work demonstrates that a minimal modification of an existing neural codec can provide plausible low-bitrate, scene-based HOA coding, and the covariance loss is a reasonable, interpretable component for spatial-quality control. The transfer-learning scheme is a practical contribution that shortens training and improves validation loss. The authors also deserve credit for using a loudspeaker-based immersive listening setup rather than only headphone or objective evaluation. However, the central suitability claim is currently supported only by a small in-corpus listening test, and the paper itself acknowledges poor behavior outside the training database. The contribution is incremental but useful if the scope of the claims is tightened to match the evidence.

major comments (3)
  1. [Experiment, cross-validation] The evaluation uses a per-scene split: "we utilize 7/8 of the samples on each scene for model training, and keep the rest separate for validation and subjective testing." Every test excerpt therefore shares its acoustic environment, recording session, microphone array, and stationary scene statistics with training material. Because the proposed covariance loss (Eq. 3) is a broadband, time-averaged interchannel correlation measure, a model can match scene-specific spatial statistics without learning a general representation of HOA ambience. The paper's own caveat in Results and Discussion that informal listening outside EigenScape indicated poor generality supports this concern. The abstract's claim that the method is "suitable for coding scene-based, 16-channel Ambisonics content" is too broad for this evidence; the authors should either run a leave-one-scene-out evaluation (even with a small number of listeners) or explicitly restrict all conclusions to within-database compression fidelity.
  2. [Results and Discussion, Fig. 2] The MUSHRA result is based on only 8 listeners and 8 items, and the paper reports only aggregate means with 95% confidence intervals. No per-item or per-listener scores are shown, no ITU-R BS.1534 outlier screening is reported, and no significance test is performed. Since the central claim is that the proposed codec "outperforms" Opus at 160 kbps, the paper should include individual score distributions, a paired statistical test (e.g., Wilcoxon signed-rank or a mixed-effects model with listener and item as random effects), and explicit mean values for each condition. As written, the reported advantage could be within listener or item variability.
  3. [Loss Functions, covariance loss] The covariance loss is presented as a novel component, but no ablation isolates its contribution. The paper states "We use weighting of 1.0 for the covariance loss" and lists the other loss weights, yet it never compares training with and without Lcov. Without such an ablation, it is unclear whether the reported spatial quality is attributable to the proposed loss or to the existing RVQGAN losses and the architecture modification. At minimum, an objective comparison (e.g., covariance error or MUSHRA scores for a no-Lcov model) should be reported to substantiate the loss's role.
minor comments (5)
  1. [Methods, Loss Functions] There is a typo in the sentence "It has been found that such preservation of the convariance structure between channels is a useful target" — "convariance" should be "covariance."
  2. [Experiment] The sentence "and the keep the rest separate for validation and subjective testing" contains an extra "the"; it should read "and keep the rest separate."
  3. [Ambisonics section] The phrase "spatial capture is often applied to background ambience and overall scene of e.g. alive event" should be "e.g. a live event."
  4. [Model Architecture, Eq. (2)] The notation in Eq. (2) is slightly confusing: the text says "C denotes a number of channels" while the input and output sizes are written as Cin and Cout. Using subscripted C throughout would be clearer.
  5. [Fig. 2] The figure does not show the numerical MUSHRA scores or the confidence interval widths; a small table with condition means and confidence intervals would make the result easier to interpret and reproduce.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central claim is an empirical listening-test outcome, and no fitted parameter or self-citation reduces to the claimed result.

full rationale

The paper's strongest claim—that the proposed multichannel RVQGAN codes 16-channel third-order Ambisonics with good quality at 16 kbps—is established by a MUSHRA listening test, not by a derivation from assumptions. The proposed covariance loss (Eq. 3) is a training objective whose weight (1.0) is hand-set rather than tuned to the test scores, and its perceptual motivation is grounded in external psychoacoustic references [30,31,32] and spatial-audio standards [9,10,34]. The multichannel architecture simply changes the first and last convolutional layers to accept 16 channels while keeping the bottleneck, so bitrate accounting is transparent. Transfer learning copies weights from the publicly available Descript monaural RVQGAN [3], which is external and not derived from the present results. The only evaluation weakness is that the cross-validation split (7/8 of samples per scene) keeps every test excerpt in the same acoustic scene used for training, limiting evidence of generalization to unseen environments. That is a validity limitation, not circularity: the MUSHRA result is a measured outcome, not a quantity forced by the training loss or by the cited prior work. Self-citations to earlier HOA compression papers [24,25,26,28] are contextual references and are not load-bearing for the new claim. Consequently, no circular step can be exhibited.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

The paper makes no first-principles derivation; it is an empirical engineering contribution. The central claim rests on standard Ambisonics theory (truncation to order 3), the Descript RVQGAN training framework with its inherited loss weightings, a perceptual assertion that inter-channel correlation governs spatial impression, and the validity of a small MUSHRA test. The only hand-set quantity introduced by the paper is the covariance loss weight (1.0), which is not ablated. No new physical entities are postulated.

free parameters (1)
  • covariance_loss_weight = 1.0
    Chosen by hand in the loss function (Methods, Loss Functions); no ablation or sensitivity analysis is provided, and the central listening test uses this fixed value.
assumptions (4)
  • domain assumption Third-order Ambisonics truncation to 16 channels adequately represents the recorded sound field for the test material.
    Invoked in the Ambisonics section: 'practical implementations necessitate truncation to a finite order M... third-order Ambisonics contains 16 channels.'
  • domain assumption Preserving the normalized inter-channel covariance matrix (Pearson correlations) preserves perceived spatial impression (envelopment, diffuseness).
    Methods, Loss Functions: 'found to be strongly related to perceived interaural coherence' and 'important descriptor of perceptual spatial impression.'
  • domain assumption The Descript RVQGAN architecture, loss weightings, and training procedure are a valid baseline for the multichannel extension.
    The method is built on the Descript codebase [3]; the paper does not re-derive or validate this baseline independently.
  • domain assumption A MUSHRA test with 8 listeners and 8 tracks yields reliable quality rankings for this comparison.
    Experiment: 'Eight listeners completed the MUSHRA test.' No statistical analysis is reported, and the small sample size is not discussed in the analysis.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Compression of Higher Order Ambisonics with Multichannel RVQGAN." pith.science (2026). https://pith.science/paper/6BHJZYAS

@misc{pith2026241112008,
  author       = {Pith},
  title        = {Pith review of: Compression of Higher Order Ambisonics with Multichannel RVQGAN},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6BHJZYAS}},
  note         = {Machine review of arXiv:2411.12008}
}
read the original abstract

A multichannel extension to the RVQGAN neural coding method is proposed, and realized for data-driven compression of third-order Ambisonics audio. The input- and output layers of the generator and discriminator models are modified to accept multiple (16) channels without increasing the model bitrate. We also propose a loss function for accounting for spatial perception in immersive reproduction, and transfer learning from single-channel models. Listening test results with 7.1.4 immersive playback show that the proposed extension is suitable for coding scene-based, 16-channel Ambisonics content with good quality at 16 kbps when trained and tested on the EigenScape database. The model has potential applications for learning other types of content and multichannel formats.

Figures

Figures reproduced from arXiv: 2411.12008 by the authors.

Figure 1
Figure 1. Example comparison between proposed transfer learning utilizing pre-trained [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. MUSHRA score mean and 95% confidence interval over 8 tracks and 8 listeners, [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

40 extracted references · 33 canonical work pages

  1. [1]

    Further investigations of high- order ambisonics and wavefield synthesis for holophonic sound imaging,

    Jerome Daniel, Sebastien Moreau, and Rozenn Nicol, “Further investigations of high- order ambisonics and wavefield synthesis for holophonic sound imaging,” in Audio Engineering Society Convention 114, March 2003

    work page 2003

  2. [2]

    SoundStream: An end-to-end neural audio codec,

    Neil Zeghidour, Alejandro Luebs, Ahmed Omran, Jan Skoglund, and Marco Tagliasac- chi, “SoundStream: An end-to-end neural audio codec,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 30, pp. 495–507, 2021

    work page 2021

  3. [3]

    High-fidelity audio compression with improved R VQGAN,

    Rithesh Kumar, Prem Seetharaman, Alejandro Luebs, Ishaan Kumar, and Kundan Kumar, “High-fidelity audio compression with improved R VQGAN,” 2023, arXiv 2306.06546

    arXiv 2023

  4. [4]

    High-fidelity diffusion-based audio codec,

    Zhengpu Zhang, Jianyuan Feng, Yongjian Mao, Yehang Zhu, Junjie Shi, Xuzhou Ye, Shilei Liu, Derong Liu, and Chuanzeng Huang, “High-fidelity diffusion-based audio codec,” 2024 18th International Workshop on Acoustic Signal Enhancement (IWAENC), pp. 344–348, 2024

    work page 2024

  5. [5]

    From Discrete Tokens to High-Fidelity Audio Using Multi-Band Diffusion

    Robin San Roman, Yossi Adi, Antoine Deleforge, Romain Serizel, Gabriel Synnaeve, and Alexandre D’efossez, “From discrete tokens to high-fidelity audio using multi-band diffusion,” 2023, arXiv 2308.02560

    work page Pith review arXiv 2023

  6. [6]

    ESC: Efficient speech coding with cross-scale residual vector quantized transformers,

    Yuzhe Gu and Enmao Diao, “ESC: Efficient speech coding with cross-scale residual vector quantized transformers,” 2024, arXiv 2404.19441

    arXiv 2024

  7. [7]

    Audio Decoding by Inverse Problem Solving

    Pedro J. Villasana T., Lars Villemoes, Janusz Klejsa, and Per Hedelin, “Audio decoding by inverse problem solving,” 2024, arXiv 2409.07858

    work page Pith review arXiv 2024

  8. [8]

    Scalable format and tools to extend the possibilities of cinema audio,

    Charles Robinson, Nicholas Tsingos, and Shripal Mehta, “Scalable format and tools to extend the possibilities of cinema audio,” SMPTE Motion Imaging Journal, vol. 121, no. 8, November 2012

    work page 2012

Show all 40 references
  1. [9]

    Digital Audio Compression (AC-4) Standard,

    “Digital Audio Compression (AC-4) Standard,” Standard, European Telecommunica- tions Standards Institute, Geneva, CH, 2018

    2018

  2. [10]

    Information technology - High efficiency coding and media delivery in heterogeneous environments - Part 3: 3D audio,

    “Information technology - High efficiency coding and media delivery in heterogeneous environments - Part 3: 3D audio,” Standard, International Organization for Standard- ization, Geneva, CH, 2022

    2022

  3. [11]

    Immersive Audio Model and Formats,

    “Immersive Audio Model and Formats,” https://aomedia.org/iamf/, 2024

    2024

  4. [12]

    Advanced sound system for programme production,

    “Advanced sound system for programme production,” Standard, International Telecommunication Union, Geneva, CH, 2022

    2022

  5. [13]

    High fidelity neural audio compression,

    Alexandre D’efossez, Jade Copet, Gabriel Synnaeve, and Yossi Adi, “High fidelity neural audio compression,” 2022, arXiv 2210.13438

    2022 arXiv

  6. [14]

    Multi- channel audio signal generation,

    W. Bastiaan Kleijn, Michael Chinen, Felicia S. C. Lim, and Jan Skoglund, “Multi- channel audio signal generation,” in ICASSP 2023 - 2023 IEEE International Confer- ence on Acoustics, Speech and Signal Processing (ICASSP), 2023, pp. 1–5

    2023

  7. [15]

    Simple and controllable music generation,

    Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi, and Alexandre D´ efossez, “Simple and controllable music generation,” 2024, arXiv 2306.05284

    2024 arXiv

  8. [16]

    Fast timing- conditioned latent audio diffusion,

    Zach Evans, CJ Carr, Josiah Taylor, Scott H. Hawley, and Jordi Pons, “Fast timing- conditioned latent audio diffusion,” 2024, arXiv 2402.04825

    2024 arXiv

  9. [17]

    Long-form music generation with latent diffusion,

    Zach Evans, Julian D. Parker, CJ Carr, Zack Zukowski, Josiah Taylor, and Jordi Pons, “Long-form music generation with latent diffusion,” 2024, arXiv 2404.10301

    2024 arXiv

  10. [18]

    Spatial redundancy in higher order ambisonics and its use for lowdelay lossless compression,

    Erik Hellerud, Audun Solvang, and U. Peter Svensson, “Spatial redundancy in higher order ambisonics and its use for lowdelay lossless compression,” in 2009 IEEE Inter- national Conference on Acoustics, Speech and Signal Processing, 2009, pp. 269–272

    2009

  11. [19]

    Frequency domain singu- lar value decomposition for efficient spatial audio coding,

    Sina Zamani, Tejaswi Nanjundaswamy, and Kenneth Rose, “Frequency domain singu- lar value decomposition for efficient spatial audio coding,” in 2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2017, pp. 126–130

    2017

  12. [20]

    Spatial audio coding with backward-adaptive sin- gular value decomposition,

    Sina Zamani and Kenneth Rose, “Spatial audio coding with backward-adaptive sin- gular value decomposition,” in Audio Engineering Society Convention 145. Audio Engineering Society, 2018

    2018

  13. [21]

    Spatial audio coding without recourse to back- ground signal compression,

    Sina Zamani and Kenneth Rose, “Spatial audio coding without recourse to back- ground signal compression,” in ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019, pp. 720–724

    2019

  14. [22]

    First-order ambisonic coding with quaternion-based interpolation of PCA rotation matrices,

    Pierre Mah´ e, Stephane Ragot, and Sylvain Marchand, “First-order ambisonic coding with quaternion-based interpolation of PCA rotation matrices,” in EAA Spatial Audio Signal Processing Symposium, Paris, France, Sept. 2019, pp. 7–12

    2019

  15. [23]

    Higher order ambisonics compression method based on independent component analysis,

    Jiahao Xu, Yadong Niu, Xihong Wu, and Tianshu Qu, “Higher order ambisonics compression method based on independent component analysis,” in Audio Engineering Society Convention 150. Audio Engineering Society, 2021

    2021

  16. [24]

    Spatial audio compression with adaptive singular value decomposition using reconstructed frames,

    Mahmoud Namazi, Ahmed Elshafiy, and Kenneth Rose, “Spatial audio compression with adaptive singular value decomposition using reconstructed frames,” in Audio Engineering Society Conference: 2022 AES International Conference on Audio for Virtual and Augmented Reality. Audio Eng...

    2022

  17. [25]

    On ultra low-delay com- pression of higher order ambisonics signals,

    Mahmoud Namazi, Ahmed Elshafiy, and Kenneth Rose, “On ultra low-delay com- pression of higher order ambisonics signals,” in 2024 Data Compression Conference (DCC), 2024, pp. 512–521

    2024

  18. [26]

    Ultra-low delay lossless compression of higher order ambisonics,

    Mahmoud Namazi and Kenneth Rose, “Ultra-low delay lossless compression of higher order ambisonics,” in ICASSP 2024 - 2024 IEEE International Conference on Acous- tics, Speech and Signal Processing (ICASSP), 2024, pp. 791–795

    2024

  19. [27]

    Perceptually- motivated spatial audio codec for higher-order ambisonics compression,

    Christoph Hold, Leo McCormack, Archontis Politis, and Ville Pulkki, “Perceptually- motivated spatial audio codec for higher-order ambisonics compression,” in ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2024, pp. 1121–1125

    2024

  20. [28]

    thesis, UC Santa Barbara, 2024

    Mahmoud Namazi, Advancements in Higher Order Ambisonics Compression and Loss Concealment Techniques, Ph.D. thesis, UC Santa Barbara, 2024

    2024

  21. [29]

    A guide to convolution arithmetic for deep learning,

    Vincent Dumoulin and Francesco Visin, “A guide to convolution arithmetic for deep learning,” 2018, arXiv 1603.07285

    2018 arXiv

  22. [30]

    Spatial hearing: The psychophysics of human sound localization (revised edition),

    Jens Blauert, “Spatial hearing: The psychophysics of human sound localization (revised edition),” 1997

    1997

  23. [31]

    Spatial mapping of intracranial auditory events for various degrees of interaural coherence,

    Jens Blauert and Werner Lindemann, “Spatial mapping of intracranial auditory events for various degrees of interaural coherence,” The Journal of the Acoustical Society of America, vol. 79, no. 3, pp. 806–813, March 1986

    1986

  24. [32]

    Human cortical processing of interaural coherence,

    Robert Luke, Hamish Innes-Brown, Jaime A Undurraga, and David Mcalpine, “Human cortical processing of interaural coherence,” iScience, vol. 5, no. 25, March 2022

    2022

  25. [33]

    Automatic multitrack mixing with a differentiable mixing console of neural audio effects,

    Christian J. Steinmetz, Jordi Pons, Santiago Pascual, and Joan Serr` a, “Automatic multitrack mixing with a differentiable mixing console of neural audio effects,” 2020, arXiv 2010.10291

    2020 arXiv

  26. [34]

    MPEG spatial audio object coding - the ISO/MPEG standard for efficient coding of interactive audio scenes,

    Jonas Engdeg ˚ ard et al., “MPEG spatial audio object coding - the ISO/MPEG standard for efficient coding of interactive audio scenes,” in AES 129th Convention. Audio Engineering Society, 2010

    2010

  27. [35]

    Eigenscape,

    Marc Ciufo Green and Damian Murphy, “Eigenscape,” https://doi.org/10.5281/ zenodo.1012809, 2017

    2017

  28. [36]

    Method for the subjective assessment of intermediate quality level of au- dio systems,

    ITU-R, “Method for the subjective assessment of intermediate quality level of au- dio systems,” Recommendation BS.1534-2, International Telecommunication Union, Geneva, 2014

    2014

  29. [37]

    Ambisonics in an Ogg Opus container,

    Jan Skoglund and Michael Graczyk, “Ambisonics in an Ogg Opus container,” https: //www.rfc-editor.org/rfc/rfc8486.html, 2018

    2018

  30. [38]

    Audio definition model renderer for advanced sound systems,

    ITU-R, “Audio definition model renderer for advanced sound systems,” Recommen- dation BS.2127-1, International Telecommunication Union, Geneva, 2023

    2023

  31. [39]

    Advanced sound system for programme production,

    ITU-R, “Advanced sound system for programme production,” Recommendation BS.2051-3, International Telecommunication Union, Geneva, 2022

    2022

  32. [40]

    Methods for the subjective assessment of small impairments in audio sys- tems,

    ITU-R, “Methods for the subjective assessment of small impairments in audio sys- tems,” Recommendation BS.1116-3, International Telecommunication Union, Geneva, 2015

    2015

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.