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arxiv: 2605.22262 · v1 · pith:65YDXXFOnew · submitted 2026-05-21 · 💻 cs.SD · cs.LG· eess.AS

Automatic Contextual Audio Denoising

Diep Luong , Konstantinos Drossos , Mikko Heikkinen , Tuomas Virtanen This is my paper

Pith reviewed 2026-05-22 02:24 UTC · model grok-4.3

classification 💻 cs.SD cs.LGeess.AS
keywords audio denoisingcontextual processingacoustic scene classificationout-of-context soundsdeep learningnoise suppressionsound events
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The pith

Context inference lets audio denoisers remove only scene-irrelevant sounds

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

The paper establishes that audio denoising should depend on inferred context rather than fixed definitions of what counts as noise. The same sound component can be relevant information in one acoustic setting and unwanted noise in another. The authors implement a deep learning method that first infers an acoustic scene class from the input and then suppresses events outside the typical distribution of that scene. Tests on paired clean and noisy recordings from varied scenes show higher objective metric scores than versions that skip context inference or receive uninformative context labels. A sympathetic reader would care because real-world listening situations require selective removal of sounds based on what the listener intends to attend to.

Core claim

By associating context with acoustic scene classes, labeling sound events outside a scene's typical distribution as out-of-context noise, and training a model to infer the scene while removing those components, the system achieves better denoising performance than non-contextual baselines, oracle-context variants, and uninformative-context variants on paired clean/noisy data where relevance of the same events changes with context.

What carries the argument

A deep learning model that jointly infers the acoustic scene class from the audio and performs context-dependent denoising by removing out-of-context sound events.

If this is right

  • Context-dependent processing yields higher scores on standard objective metrics than fixed-target denoising.
  • The model infers context from the audio signal without requiring separate context labels at inference time.
  • Performance gains appear precisely where out-of-context components in one scene become in-context in another.
  • Oracle context knowledge sets an upper performance bound while inferred context approaches it.

Where Pith is reading between the lines

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

  • The same out-of-context versus in-context distinction could be tested on continuous streaming audio rather than fixed clips.
  • Neighbouring tasks such as audio enhancement or source separation might adopt similar scene-based relevance filtering.
  • Applications like surveillance or personal recording devices would automatically preserve different sound sets depending on detected scene.

Load-bearing premise

Acoustic scene class provides a sufficient and stable proxy for listener-relevant context so that events outside the scene distribution can be treated as removable noise without losing information a listener would want to keep.

What would settle it

A test set of recordings from a given scene class in which the model removes sounds that match listener intent for that specific use case or fails to remove sounds that do not match it.

Figures

Figures reproduced from arXiv: 2605.22262 by Diep Luong, Konstantinos Drossos, Mikko Heikkinen, Tuomas Virtanen.

Figure 1
Figure 1. Figure 1: Overview of our ACAD method. Context extrac [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: t-SNE representation of the bottleneck features [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: t-SNE representation of the bottleneck features [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
read the original abstract

Audio context determines which sound components and sources are relevant and which can be perceived as irrelevant (noise) by listeners. For example, traffic noise is informative in urban surveillance but noise for a phone call at the same location. Most current audio denoising systems apply fixed target-noise definitions, often removing useful components in one context while failing to suppress irrelevant components. To address this, we introduce the concept automatic contextual audio denoising (ACAD) which defines target and noise based on the inferred context. In this work, we restrict context to be associated with an acoustic scene class. We label sound events outside the event distribution of a scene class (noise) as out-of-context (OC) and events typical for that scene as in-context (IC). We implement a deep learning method that automatically infers the context of the audio signal and removes OC components, and benchmark it against variants: without context inference, with oracle context, and with separately provided uninformative context. On paired clean/noisy data across diverse contexts, where OC components in one context may be IC in another, our proposed method outperforms other approaches across standard objective metrics, indicating that the model can infer context and context-dependent processing can enhance denoising.

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 / 2 minor

Summary. The paper proposes automatic contextual audio denoising (ACAD) by restricting context to acoustic scene classes. It labels out-of-context (OC) events as those outside the typical event distribution of a scene class and in-context (IC) events as those typical for the class. A deep neural network is trained to infer the scene context from the audio and remove OC components while preserving IC ones. On paired clean/noisy data spanning diverse scenes, the method is benchmarked against variants lacking context inference, using oracle context, or supplied with uninformative context, and is reported to outperform them on standard objective metrics, suggesting successful context inference and context-dependent denoising.

Significance. If the central claim holds, the work offers a practical route to context-aware denoising that could improve real-world applications where sound relevance is scene-dependent, such as surveillance or mobile communication. By leveraging scene class as a proxy and existing paired datasets, the approach avoids the need for explicit intent labels during training. The reported gains over strong baselines indicate potential for adaptive processing beyond fixed target-noise definitions.

major comments (2)
  1. [Abstract] Abstract and results section: The claim that outperformance on objective metrics demonstrates that the model infers context and that context-dependent processing enhances denoising is not fully supported by the evaluation design. OC/IC labels are assigned solely according to event distributions within each scene class, without task or intent supervision. This leaves open the possibility that gains arise from a more flexible general-purpose denoiser rather than genuine context inference, especially since the paper's own example (traffic informative for surveillance but noise for a phone call) shows that identical scenes can require opposite OC/IC treatment depending on listener intent.
  2. [§4 Experiments] Methods and experimental setup: No ablation or analysis isolates whether the network learns scene-specific processing rules versus simply learning a broader denoising mapping. The oracle-context and uninformative-context baselines are useful, but without additional controls (e.g., scene-agnostic but high-capacity models or explicit measurement of scene-classification accuracy correlated with denoising improvement), it remains unclear whether the performance edge stems from context inference per se.
minor comments (2)
  1. [§3 Method] Clarify in the methods section how scene labels are obtained or inferred during inference when only the noisy mixture is available, as this is central to the automatic aspect of ACAD.
  2. [Abstract] The abstract mentions 'diverse contexts' but does not specify the number of scene classes or the criteria for selecting them; adding this detail would strengthen reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We address each major comment below with clarifications based on the existing evaluation design and indicate the revisions we will incorporate.

read point-by-point responses

  1. Referee: [Abstract] Abstract and results section: The claim that outperformance on objective metrics demonstrates that the model infers context and that context-dependent processing enhances denoising is not fully supported by the evaluation design. OC/IC labels are assigned solely according to event distributions within each scene class, without task or intent supervision. This leaves open the possibility that gains arise from a more flexible general-purpose denoiser rather than genuine context inference, especially since the paper's own example (traffic informative for surveillance but noise for a phone call) shows that identical scenes can require opposite OC/IC treatment depending on listener intent.

    Authors: We acknowledge that OC/IC labels derive from scene-class event distributions without explicit intent or task supervision, as this is the deliberate scope of the work to leverage existing paired datasets. The uninformative-context baseline controls for a general-purpose denoiser by supplying equivalent model capacity with non-informative context, while the oracle-context baseline establishes an upper bound. Outperformance relative to the uninformative baseline therefore ties the gains to the use of inferred scene context rather than increased flexibility alone. We agree that the traffic example illustrates how intent can invert OC/IC assignments within the same scene; our method uses scene class strictly as a proxy and does not model full listener intent. We will revise the abstract and results section to state the findings more precisely as evidence of scene-class context inference and context-dependent denoising within this proxy framework, and we will add an explicit limitations paragraph on intent. revision: partial

  2. Referee: [§4 Experiments] Methods and experimental setup: No ablation or analysis isolates whether the network learns scene-specific processing rules versus simply learning a broader denoising mapping. The oracle-context and uninformative-context baselines are useful, but without additional controls (e.g., scene-agnostic but high-capacity models or explicit measurement of scene-classification accuracy correlated with denoising improvement), it remains unclear whether the performance edge stems from context inference per se.

    Authors: The uninformative-context baseline already functions as a scene-agnostic control with matched capacity. To further isolate the contribution of context inference, we will add a new analysis in the revised §4 that reports the correlation between per-scene classification accuracy of the context-inference module and the corresponding denoising metric gains. This will provide direct evidence linking context inference quality to performance improvements. revision: yes

Circularity Check

0 steps flagged

No circularity: standard supervised training and external evaluation

full rationale

The paper defines context as acoustic scene class and OC/IC labels via event distribution within each class, then trains a neural network on paired clean/noisy data with scene labels to infer context and remove OC components. Performance is measured with standard objective metrics against explicit baselines (no-context, oracle-context, uninformative-context variants). No equations or claims reduce the reported gains to quantities defined inside the model by construction, nor does any load-bearing step rest on a self-citation chain that itself lacks independent verification. The derivation chain is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 1 invented entities

The central claim rests on the assumption that scene class provides a usable definition of relevance, that out-of-context events can be identified from scene-typical distributions, and that standard deep learning training on paired data is sufficient to learn both inference and removal. No new physical entities are postulated. The main free parameters are the neural network weights learned from data.

free parameters (1)
  • neural network weights
    Standard learned parameters of the deep learning model that performs both context inference and denoising; fitted during training on the paired dataset.
axioms (2)
  • domain assumption Acoustic scene class is a sufficient proxy for listener-relevant context
    The paper restricts context to scene class and treats events outside the scene's typical distribution as removable noise.
  • domain assumption Standard supervised training on paired clean/noisy data can learn context-dependent denoising
    The method is implemented as a deep learning model trained end-to-end on the described data.
invented entities (1)
  • out-of-context (OC) vs in-context (IC) sound events no independent evidence
    purpose: To label sounds as noise or target based on inferred scene class rather than fixed definitions
    New labeling scheme introduced to operationalize context-dependent denoising; no independent falsifiable evidence outside the model's own predictions is provided.

pith-pipeline@v0.9.0 · 5751 in / 1820 out tokens · 34481 ms · 2026-05-22T02:24:44.602082+00:00 · methodology

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Reference graph

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