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REVIEW 3 major objections 5 minor 2 cited by

Stereo Sound Event Localization and Detection with Onscreen/offscreen Classification

T0 review · 3 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Stereo audio alone can support sound event localization and detection in the left-right plane, according to the new DCASE2025 Task 3 dataset and baselines.

desk verdict A genuinely useful stereo SELD challenge setup, but the on/off ground truth may hinge on ordering of azimuth folding versus FOV check—verify the code before trusting Track B. read the letter →

arxiv 2507.12042 v1 pith:LRSIJXL4 submitted 2025-07-16 cs.SD cs.CVcs.MMeess.ASeess.IV

Kazuki Shimada , Archontis Politis , Iran R. Roman , Parthasaarathy Sudarsanam , David Diaz-Guerra , Ruchi Pandey , Kengo Uchida , Yuichiro Koyama
show 5 more authors
Naoya Takahashi Takashi Shibuya Shusuke Takahashi Tuomas Virtanen Yuki Mitsufuji
This is my paper · ORCID
classification cs.SDcs.CVcs.MMeess.ASeess.IV
keywords stereosoundeventlocalizationanddetectionDCASE2025Task3mid-sideconversiononscreen/offscreenclassificationdistanceestimationmulti-ACCDOAaudiovisualfusionSTARSS23
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 establishes a new challenge task: detecting and localizing sound events from ordinary stereo audio, the format of most media content. It argues that even though stereo has inherent front-back and top-bottom ambiguities, a system can still estimate an event's left-right azimuth and its distance, and it introduces a dataset of 40,000 five-second clips converted from real spatial recordings to support that claim. The paper also adds a sub-task for audiovisual input: deciding whether each sound source is onscreen or offscreen, which matters when the video sees only a 100-degree field of view. The baseline results show stereo SELD is feasible, with the audiovisual model exceeding the audio-only model on detection and angular accuracy but not on distance, and an onscreen/offscreen accuracy of 80 percent that largely mirrors the dataset's 1:3 onscreen ratio.

What carries the argument

The load-bearing conversion is the mid-side stereo extraction $L(n) = W(n) + Y(n)$, $R(n) = W(n) - Y(n)$, which turns the zeroth-order omnidirectional signal and left-right dipole of ACN/SN3D FOA into a coincident stereo pair. Around this sit three other mechanism pieces: the folding of back-hemisphere azimuths to the front to remove the front-back ambiguity, the multi-ACCDOA output format that couples class activity with Cartesian DOA and distance regressors so overlapping same-class events can be tracked, and the localization-dependent F-score with a 20-degree and relative-distance-1 threshold that ranks systems. For the audiovisual track, a pre-trained ResNet-50 feature stream is fused with audio through transformer cross-attention, and a binary neuron predicts onscreen/offscreen status. Together these define the task's input, output, and success measure.

What would settle it

Count, across the development set, the frames in which two simultaneously active sources have back-hemisphere azimuths that fold onto the same front angle; if such collisions are frequent, the folded labels are systematically ambiguous. Alternatively, run the baseline on genuine coincident-pair or binaural stereo recordings with known source positions and compare the localization F-score with the FOA-derived test set; a large drop would show the synthetic stereo conversion does not stand in for real stereo media.

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Extended reading notes

Core claim

The central claim is that stereo-only audio carries enough spatial information to support sound event localization and detection, provided the task is reformulated around the left-right axis. The dataset is built by taking STARSS23 first-order Ambisonics and perspective-video recordings, rotating them to a random viewing direction, and converting the four-channel audio to mid-side stereo with $L = W + Y$ and $R = W - Y$; azimuth labels in the back hemisphere are folded to the front because stereo cannot distinguish front from back, and elevation labels are discarded. The baseline uses a convolutional recurrent network with multi-head attention and a multi-ACCDOA output that predicts up to three simultaneous events per class with Cartesian $[x, y]$ DOA coordinates and a distance value. Evaluated on held-out clips, the audio-only baseline reaches a macro localization-dependent F-score of 26.1 percent and the audiovisual baseline 27.5 percent, with onscreen/offscreen accuracy near 78 percent. The paper's stated conclusion is that stereo SELD is feasible and the dataset supports reproducible training and evaluation.

Load-bearing premise

The whole benchmark rests on the assumption that stereo signals produced by the mid-side equations from four-channel recordings behave like stereo from real media, and that folding rear sounds forward does not create contradictory labels.

Editorial extensions

If this is right

  • SELD can be evaluated on ordinary media audio rather than specialized four-channel formats, widening the task to consumer recordings and video content.
  • A system that only sees the left-right axis can still localize events well enough to beat chance, with the audio-only baseline reaching a macro F20°/1 of 26.1 percent on the evaluation set.
  • The audiovisual track's ranking metric now requires a correct onscreen/offscreen call for a detection to count, so visual context is scored as part of localization.
  • Distance estimation remains the weakest link; replacing the model's distance with the class-average distance of the estimated class improves or matches the baselines, indicating the models have not learned true distance cues.

Reading between the lines

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

  • The paper does not test on stereo recorded natively; a natural extension is to record or collect true mid-side or binaural material and see whether the FOA-derived stereo baseline transfers.
  • Because the viewing angle is chosen randomly and 77.5 percent of sources are offscreen, an onscreen/offscreen model can score near 80 percent by always guessing 'offscreen'; future splits should balance onscreen events to make the metric informative.
  • Folding back azimuths means a sound at +120 degrees and one at +60 degrees both map to the same front label; an ambiguity-aware training loss or a front/back confidence output might extract more spatial information than the current framing.
  • The distance bias analysis suggests that class-conditioned distance priors are a strong baseline for this dataset, so future systems should be compared against such priors to demonstrate genuine distance learning.
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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. This paper introduces the DCASE2025 Task 3 stereo sound event localization and detection (SELD) challenge, including a new audiovisual sub-task of onscreen/offscreen classification. The authors construct the DCASE2025 Task3 Stereo SELD Dataset by converting STARSS23 FOA recordings to M/S stereo audio and 360-degree video to perspective video with a fixed 100-degree horizontal FOV. They describe a baseline CRNN audio-only model and an audiovisual model with a ResNet-50 visual encoder and transformer fusion, along with modified evaluation metrics: azimuth-only DOAE, relative distance error, onscreen/offscreen accuracy, and a localization-dependent F-score that incorporates an on/off-screen correctness requirement for the audiovisual track. Baseline results are reported on development and evaluation splits, and a dataset-bias analysis shows that the onscreen/offscreen accuracy of the audiovisual baseline is close to the 77.5% always-offscreen prior and that distance estimation underperforms a class-average bias baseline.

Significance. The paper is a challenge description and baseline paper, not a methods paper, and it offers a substantial community resource: a publicly released dataset, a reproducible data-generation pipeline, baseline code, and a transparent bias analysis. The M/S stereo conversion equations (1)-(2) are simple and correct given the stated ACN/SN3D convention, and the evaluation protocol is clearly specified. If the ground-truth construction is sound, the dataset enables a new research direction of stereo SELD for regular media content. The paper also honestly reports that the audiovisual baseline struggles to exploit visual information, which is a useful finding for the community. However, the central claim that stereo SELD is feasible and shows promise rests on single-run baseline scores whose statistical reliability is not established, and one aspect of the label-generation pipeline creates a potential correctness risk for the audiovisual ground truth.

major comments (3)
  1. [Section 2.2] The order of operations for computing the onscreen/offscreen labels is critically ambiguous. The text states that rotated azimuth labels pointing to the back hemisphere are folded front-back, and then 'we compare the new DOA labels with the azimuthal range of the FOV'. If 'new' means post-fold, then a physically offscreen source behind the camera (e.g., azimuth 170 degrees) folds to 10 degrees and is labeled onscreen even though it is not visible in the perspective video. Because the M/S conversion in Eqs. (1)-(2) discards the X (front-back) component, the stereo audio for a source at 170 degrees is identical to that at 10 degrees; a model trained on such labels receives contradictory audiovisual targets. The reported audiovisual onscreen/offscreen accuracy of 80.0% (Table 1) and 77.8% (Table 2) is close to the 77.5% always-offscreen prior, so the ambiguity is not resolved by the experimental results. Please state explicitly whether FOV membership is computed from the rotated azimuth before or after front-back folding, and if necessary release a corrected version of the dataset labels.
  2. [Section 6] All results in Tables 1 and 2 are reported as single-run scores without error bars, confidence intervals, or significance tests. Several comparisons are numerically close: the audiovisual model outperforms the audio-only model by 4.0 points in macro F20/1 on the development set but by only 1.4 points on the evaluation set, and the onscreen/offscreen accuracy is within a few points of the 77.5% majority-class prior as the paper itself notes in Section 7. In the absence of repeated-run variability or a paired test, the conclusion in Section 8 that 'stereo SELD is feasible, with initial results indicating promise' is not strongly supported. I recommend adding multiple training runs or at least a clear statement that the reported numbers are single-run and should be interpreted cautiously.
  3. [Section 7] The dataset-bias analysis is a strength, but the statement that the onscreen/offscreen accuracy 'is around a random guess adjusted to the data distribution' is under-specified. A classifier that always predicts the majority class would achieve approximately 77.5% accuracy, and the audiovisual baseline achieves 80.0% / 77.8%. To better assess whether the model learns any onscreen/offscreen information, the paper should report balanced accuracy, ROC-AUC, or per-class accuracies in addition to the raw accuracy; otherwise the difference of 2.5 points or 0.3 points is difficult to interpret as evidence of learning. This is especially important because the onscreen/offscreen correctness is part of the primary ranking metric F20/1/onoff in the audiovisual track.
minor comments (5)
  1. [Section 5] There is a typo in 'Apart form last-year metrics' which should be 'Apart from last-year metrics'.
  2. [Section 5] The phrase 'in for the audiovisual track' is ungrammatical; it should likely be 'and for the audiovisual track' or 'for the audiovisual track'.
  3. [Section 2.2] The sentence 'The details of the sampling procedure are provided below' is redundant because the sampling details immediately follow; consider removing it or restructuring the paragraph.
  4. [Figure 1] The example captions describe left/right positions of speakers, but the figure would benefit from marking the onscreen/offscreen labels and the FOV boundary so readers can visually verify the conversion described in Section 2.2.
  5. [Section 3] The description of the multi-ACCDOA output as Cartesian [x,y] coordinates is a bit confusing given that the paper earlier states DOA estimation is azimuth-only; please clarify that the y-component here represents the front-back dimension and is intentionally underdetermined by stereo audio, or explain how the baseline handles this ambiguity during training.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the stereo SELD dataset, baselines, and metrics are empirically constructed and evaluated against held-out data.

full rationale

The paper's derivation chain is empirical rather than formal. Stereo audio is generated by the explicit mid-side conversion in Equations (1)-(2), metadata is derived from STARSS23 labels, and the baseline is trained on development clips and evaluated on a separate evaluation set. The central claim, that stereo SELD is feasible, is supported by measured F-scores, DOAE, and RDE values; no fitted parameter is relabeled as a prediction and no result follows from its own definition by construction. Self-citations to STARSS23, the DCASE2024 baseline, multi-ACCDOA, and SpatialScaper are normal methodological continuity: those cited items are released datasets, code, or peer-reviewed workshop/papers, not unverified uniqueness theorems imported to force a choice. Section 7 explicitly acknowledges dataset biases, including that the onscreen/offscreen accuracy of about 80% is close to the 77.5% always-offscreen prior, so the audiovisual result is not presented as a success that depends on a circular label definition. The only flagged concern is a potential internal-validity issue in Section 2.2: if onscreen/offscreen labels are computed after folding back-hemisphere azimuths to the front, an event physically behind the camera could be labeled onscreen, which would be a ground-truth inconsistency rather than a circular derivation. The released data-generation code makes this checkable, and the paper's own bias discussion prevents the claim from being insulated by construction. Overall, no circular step reduces the paper's conclusions to its inputs.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central claim rests on the validity of the derived stereo and perspective video representations and on the inherited STARSS23 annotations. No new physical or conceptual entities are introduced; the dataset is a constructed resource, not a postulated entity.

free parameters (3)
  • DOA error threshold = 20 degrees
    Chosen for evaluation; determines whether a detection counts as a true positive in the localization-dependent F-score.
  • Relative distance error threshold = 1
    Chosen for evaluation; bounds acceptable distance estimates in true positive counting.
  • Horizontal field of view = 100 degrees
    Chosen for the perspective video; determines onscreen/offscreen labels and audio-visual alignment.
assumptions (4)
  • domain assumption STARSS23 recordings and annotations are accurate and complete.
    The stereo dataset and all event labels are derived from STARSS23; any annotation errors propagate to the new dataset.
  • standard math Equations (1) and (2) correctly emulate a mid-side stereo microphone from FOA signals.
    This is a standard signal processing relation, but its representativeness of real stereo media is assumed without validation.
  • domain assumption The py360convert and SpatialScaper libraries function as intended.
    Used for perspective video conversion and synthetic data generation; the paper does not validate their outputs independently.
  • domain assumption Pre-trained ResNet-50 features are informative for the video frame content.
    The audiovisual baseline relies on transfer learning from ImageNet without fine-tuning analysis.

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Cite this review

Pith. "Pith review of Stereo Sound Event Localization and Detection with Onscreen/offscreen Classification." pith.science (2026). https://pith.science/paper/LRSIJXL4

@misc{pith2026250712042,
  author       = {Pith},
  title        = {Pith review of: Stereo Sound Event Localization and Detection with Onscreen/offscreen Classification},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LRSIJXL4}},
  note         = {Machine review of arXiv:2507.12042}
}
read the original abstract

This paper presents the objective, dataset, baseline, and metrics of Task 3 of the DCASE2025 Challenge on sound event localization and detection (SELD). In previous editions, the challenge used four-channel audio formats of first-order Ambisonics (FOA) and microphone array. In contrast, this year's challenge investigates SELD with stereo audio data (termed stereo SELD). This change shifts the focus from more specialized 360{\deg} audio and audiovisual scene analysis to more commonplace audio and media scenarios with limited field-of-view (FOV). Due to inherent angular ambiguities in stereo audio data, the task focuses on direction-of-arrival (DOA) estimation in the azimuth plane (left-right axis) along with distance estimation. The challenge remains divided into two tracks: audio-only and audiovisual, with the audiovisual track introducing a new sub-task of onscreen/offscreen event classification necessitated by the limited FOV. This challenge introduces the DCASE2025 Task3 Stereo SELD Dataset, whose stereo audio and perspective video clips are sampled and converted from the STARSS23 recordings. The baseline system is designed to process stereo audio and corresponding video frames as inputs. In addition to the typical SELD event classification and localization, it integrates onscreen/offscreen classification for the audiovisual track. The evaluation metrics have been modified to introduce an onscreen/offscreen accuracy metric, which assesses the models' ability to identify which sound sources are onscreen. In the experimental evaluation, the baseline system performs reasonably well with the stereo audio data.

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Forward citations

Cited by 2 Pith papers

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  2. Dual-BEATs: Unlocking Zero-Shot Stereo Audio Perception in Audio Large Language Models via Dithering

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    Uncorrelated dither noise lets dual frozen BEATs encoders preserve inter-channel amplitude differences across LLM normalizers, yielding up to 97% left/center/right accuracy and zero-shot spatial generalization.

Reference graph

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    A. S. Roman, A. Chang, G. Meza, and I. R. Roman, “Generating diverse audio-visual 360 soundscapes for sound event localization and detection,” arXiv preprint arXiv:2504.02988 , 2025

    2025 arXiv

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.