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arxiv: 2605.10153 · v1 · submitted 2026-05-11 · 💻 cs.SD · cs.LG

Recognition: 3 theorem links

· Lean Theorem

APEX: Audio Prototype EXplanations for Classification Tasks

Piotr Kawa , Kornel Howil , Piotr Borycki , Mi{\l}osz Adamczyk , Przemys{\l}aw Spurek , Piotr Syga
Authors on Pith no claims yet

Pith reviewed 2026-05-12 03:02 UTC · model grok-4.3

classification 💻 cs.SD cs.LG
keywords audio classificationexplainable AIprototype explanationspost-hoc methodsspectrogramsacoustic similarityinterpretability
0
0 comments X

The pith

APEX generates post-hoc explanations for audio classifiers by disentangling prototypes into four acoustic perspectives without retraining the model.

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

The paper introduces APEX as a framework to interpret decisions from pre-trained audio classification models. It finds representative audio examples as prototypes and separates explanations into four views that focus on different aspects of sound: localization of short events, patterns over time, emphasis on certain frequencies, and combinations of time and frequency. The approach requires no changes to the original model and leaves its predictions unchanged. This matters because audio signals differ from images in fundamental ways, so methods borrowed from vision often miss key acoustic properties and deliver less clear insights.

Core claim

APEX is a post-hoc framework for interpreting pre-trained audio classifiers by generating explanations based on prototypes disentangled into four perspectives: square-based prototypes to localize transient events, time-based prototypes for temporal patterns, frequency-based prototypes highlighting spectral bands, and time-frequency-based prototypes integrating both. These explanations respect acoustic properties, provide greater semantic clarity than standard gradient-based methods, require no fine-tuning of the backbone, and strictly preserve output invariance.

What carries the argument

Disentanglement of acoustic similarity into four prototype perspectives: square-based for transient events, time-based for temporal patterns, frequency-based for spectral bands, and time-frequency-based for integrated analysis.

If this is right

  • Explanations become available for any existing audio classifier without additional training steps.
  • Transient events can be localized while temporal patterns and spectral bands are highlighted separately or together.
  • Model decisions in audio tasks gain semantic clarity that respects the multidimensional nature of sound.
  • Deployment of audio AI systems can proceed with example-based interpretations that maintain original performance.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The four perspectives might combine into a single interactive interface for users exploring complex audio scenes.
  • Similar disentanglement could apply to other time-series data such as sensor readings or physiological signals.
  • Quantitative metrics for semantic clarity could be developed by measuring human agreement on what the prototypes reveal.

Load-bearing premise

Acoustic similarity can be captured and disentangled into the four proposed prototype perspectives without fine-tuning the backbone model while preserving output invariance.

What would settle it

An experiment on standard audio datasets where the APEX explanations either alter the classifier output or fail to show clearer semantic understanding than gradient-based methods applied to spectrograms.

Figures

Figures reproduced from arXiv: 2605.10153 by Kornel Howil, Mi{\l}osz Adamczyk, Piotr Borycki, Piotr Kawa, Piotr Syga, Przemys{\l}aw Spurek.

Figure 1
Figure 1. Figure 1: Overview of the APEX framework. Unlike traditional prototype-based approaches that require training specialized architectures from scratch, APEX operates in a post-hoc set￾ting, providing interpretability for arbitrary pre-trained audio backbones. The diagram illustrates our four distinct prototype extraction schemes: Square-based, Time-based, Frequency￾based, and Time-Frequency-based. These schemes disent… view at source ↗
Figure 2
Figure 2. Figure 2: Architectural and representational comparison between a standard audio classifier and the post-hoc APEX framework. Top: While a classical backbone produces entangled feature maps, APEX inserts a Disentanglement Module. Applying a learnable invertible transformation U and its inverse U −1 reorganizes the latent space while strictly preserving the original model’s predictions (output invariance). Bottom: The… view at source ↗
Figure 3
Figure 3. Figure 3: Explanations for a Golden-crowned Kinglet (gockin) before and after APEX optimization. Prior to tuning, the extracted prototypes resemble random noise and offer little interpretability. Following APEX optimization, prototypes become semantically meaningful, aligning with the distinct acoustic features present in the input spectrogram. Labels above prototypes show their classes. sic data by operating on sou… view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative comparison of post-hoc interpretability methods conducted on a representation learned on top of a pretrained ConvNeXt classifier. The input spectrogram (far left) contains distinct correctly classified test sounds from the SNE test set of the BirdSet [26] dataset. APEX framework successfully disentangles the latent space to generate highly localized, semantically clear time-frequency explanatio… view at source ↗
Figure 5
Figure 5. Figure 5: Depiction of the APEX masking strategy used to eval￾uate feature importance. The columns show the original spec￾trogram (left), the APEX explanation with the localized proto￾type region highlighted in green (middle), and the correspond￾ing masked spectrogram (right) for each of the schemes. Optimization via Scheme-Dependent Purity The objec￾tive of the disentanglement process is to maximize the “purity” of… view at source ↗
Figure 6
Figure 6. Figure 6: APEX explanations (ConvNeXt) for correctly classi￾fied real audio from LJSpeech [28]. The left column shows input query spectrograms and heatmaps; the right four show proto￾typical parts and labels. Odd rows display original spectro￾grams, while even rows highlight spectral differences between the real and HiFi-GAN vocoded audio. (Trained on the HiFi￾GAN subset of WaveFake [29]). spectrograms by overlaying… view at source ↗
Figure 7
Figure 7. Figure 7: Comparison of explanations between APEX (our) and prototype-based model AudioProtoPNet. The comparison is conducted on a representation learned on top of a pretrained ConvNeXt. This example shows correctly classified test sound of a mouchi (Mountain Chickadee) from the SNE test set of BirdSet [26] dataset. Labels above prototypes show their classes [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
read the original abstract

Explainable AI (XAI) has achieved remarkable success in image classification, yet the audio domain lacks equally mature solutions. Current methods apply vision-based attribution techniques to spectrograms, overlooking fundamental differences between visual and acoustic signals. While prototype reasoning is promising, acoustic similarity remains multidimensional. We introduce APEX (Audio Prototype EXplanations), a post-hoc framework for interpreting pre-trained audio classifiers. Crucially, APEX requires no fine-tuning of the original backbone and strictly preserves output invariance. APEX disentangles explanations into four perspectives: Square-based prototypes to localize transient events, Time-based for temporal patterns, Frequency-based highlighting spectral bands, and Time-Frequency-based integrating both. This yields intuitive, example-based explanations that respect acoustic properties, providing greater semantic clarity than standard gradient-based methods.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper introduces APEX, a post-hoc framework for interpreting pre-trained audio classifiers. It disentangles explanations into four prototype perspectives—square-based (transient events), time-based (temporal patterns), frequency-based (spectral bands), and time-frequency-based (joint structure)—without fine-tuning the backbone model and while strictly preserving output invariance. The method is positioned as yielding intuitive, example-based explanations that respect acoustic properties and provide greater semantic clarity than standard gradient-based attribution techniques applied to spectrograms.

Significance. If the four-perspective disentanglement can be shown to be faithful to the frozen classifier while respecting acoustic multidimensionality, APEX would address a clear gap in mature XAI methods for audio domains. The post-hoc, no-fine-tuning design and invariance guarantee are strengths that could enable broader adoption for tasks where acoustic similarity is not easily captured by vision-derived gradients.

major comments (2)
  1. [Abstract and §3] Abstract and §3 (Method): The central claim that prototypes can be discovered post-hoc to separately capture transient events, temporal patterns, spectral bands, and joint time-frequency structure—while strictly preserving output invariance and without any fine-tuning—lacks an explicit algorithm, projection step, or invariance proof. Because the backbone latent space may entangle these acoustic dimensions, it is unclear whether the separation is achieved solely on existing representations or requires implicit optimization that would violate the no-fine-tuning guarantee.
  2. [§4] §4 (Experiments): No quantitative validation, ablation studies, or comparison results against gradient-based methods are described to demonstrate that the four perspectives deliver greater semantic clarity or maintain fidelity to the original model's decisions. The abstract's claim of superior clarity therefore rests on an unverified assumption whose failure would collapse the four-perspective framing.
minor comments (1)
  1. [Abstract] The abstract refers to 'acoustic similarity remains multidimensional' but does not define how the four perspectives are formally distinguished or selected from the representation space.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our manuscript. We address each major comment below and commit to substantial revisions that will strengthen the technical presentation and empirical support for APEX.

read point-by-point responses

  1. Referee: [Abstract and §3] Abstract and §3 (Method): The central claim that prototypes can be discovered post-hoc to separately capture transient events, temporal patterns, spectral bands, and joint time-frequency structure—while strictly preserving output invariance and without any fine-tuning—lacks an explicit algorithm, projection step, or invariance proof. Because the backbone latent space may entangle these acoustic dimensions, it is unclear whether the separation is achieved solely on existing representations or requires implicit optimization that would violate the no-fine-tuning guarantee.

    Authors: We acknowledge that the original presentation of the method in §3 could benefit from greater explicitness. APEX operates entirely post-hoc: prototype discovery is performed by optimizing a small set of learnable parameters in the input domain (audio waveforms or spectrograms) while the backbone classifier remains completely frozen. The four perspectives are realized by applying dimension-specific regularization terms and masking operations during this optimization (square masks for transients, temporal slicing for time-based, frequency-band constraints for spectral, and joint 2D constraints for time-frequency). A projection step maps the optimized prototypes back to valid acoustic signals to enforce output invariance. We will add a formal algorithm box, a detailed description of the projection operator, and a short proof that the classifier output is unchanged when the prototype is substituted for the original input. Regarding entanglement, the separation does not require modifying the latent space; it exploits the fact that the pre-trained model already encodes separable acoustic cues, which we isolate via the constrained search rather than through implicit fine-tuning. revision: yes

  2. Referee: [§4] §4 (Experiments): No quantitative validation, ablation studies, or comparison results against gradient-based methods are described to demonstrate that the four perspectives deliver greater semantic clarity or maintain fidelity to the original model's decisions. The abstract's claim of superior clarity therefore rests on an unverified assumption whose failure would collapse the four-perspective framing.

    Authors: We agree that the current experimental section relies primarily on qualitative illustrations and that this is insufficient to substantiate the claims of greater semantic clarity and fidelity. In the revised manuscript we will introduce quantitative evaluations on standard audio classification benchmarks, including: (i) fidelity metrics that measure the change in model output when prototypes are used as explanations, (ii) ablation studies isolating the contribution of each of the four perspectives, and (iii) direct comparisons against gradient-based attribution methods applied to spectrograms using both automated metrics (insertion/deletion AUC) and human-subject ratings of interpretability. These additions will be placed in an expanded §4 and will directly test whether the four-perspective disentanglement improves upon vision-derived baselines while preserving invariance. revision: yes

Circularity Check

0 steps flagged

No circularity: post-hoc framework with no fitted predictions or self-referential reductions

full rationale

The paper presents APEX as a post-hoc, no-fine-tuning method that strictly preserves output invariance while disentangling acoustic similarity into four prototype perspectives. No equations, loss functions, parameter-fitting steps, or self-citations appear in the abstract or described claims that would make any output equivalent to its inputs by construction. The four perspectives are introduced as a design choice for semantic clarity rather than derived from a self-definitional loop or a fitted input renamed as a prediction. The derivation chain remains self-contained and externally falsifiable via the frozen backbone's behavior, with no load-bearing uniqueness theorems or ansatzes smuggled through prior author work.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The central claim rests on the unstated premise that prototype selection in the four acoustic domains can be performed post-hoc while exactly preserving classifier outputs; no free parameters, axioms, or invented entities are specified in the abstract.

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Reference graph

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