Audio-Contribution Filtering

• Audio-Contribution Filtering is a design pattern that determines which audio-derived contributions are allowed to impact downstream tasks through techniques like hard gating, reweighting, and conditional extraction.
• Key formulations include query-level gating, sample-level curation, and feature-level filtering, with methods such as spectrotemporal reweighting and contextual denoising addressing varied audio challenges.
• Its applications span audio-guided segmentation, training data curation, and environmental denoising, highlighting trade-offs between data quality, diversity, and overall model performance.

Searching arXiv for recent and foundational papers on audio-contribution filtering and related formulations. arxiv_search(query="audio contribution filtering denoising audio-guided segmentation noisy labels source separation contextual denoising", max_results=10) Audio-contribution filtering, in the papers considered here, is best treated as an Editor’s term for a family of operations that determine which audio-derived information is permitted to influence a downstream estimate, prediction, or training signal. The filtered object changes with task: it may be an audio-conditioned query, a generated clip, a weakly labeled example, a time-frequency region, a target source, a multidevice observation, or the inferred degree to which a model’s answer actually depends on audio. The literature does not present a single canonical definition; instead it realizes the idea as hard query rejection, rank-based sample selection, spectrotemporal reweighting, conditional source extraction, context-dependent denoising, and post-hoc attribution analysis (Wang et al., 27 Apr 2026, Hai et al., 23 Sep 2025, Lai et al., 2018, Gfeller et al., 2020, Luong et al., 21 May 2026, Morais et al., 25 Sep 2025).

1. Scope and recurrent formulations

An early file-level formulation appears in "Automatic Organisation, Segmentation, and Filtering of User-Generated Audio Content" (Mordido et al., 2017), which proposes methods that, using solely information retrieved by audio fingerprinting, group user-generated recordings containing a common excerpt, estimate how files are correlated in time and quality within an event, and use supervised learning to detect incorrect fingerprint matches. Even at this level, filtering is not yet denoising in the narrow sense; it is admission control over which audio files or pairings should be treated as valid evidence for a shared event.

Across the later literature, the same structural question reappears at different granularities. Some methods filter whether an audio query should contribute anything at all to a multimodal output. Others filter whether a generated or weakly labeled audio sample is trustworthy enough to enter training. Others alter how frequency bands or time-frequency cells contribute to a representation. Still others isolate a target source contribution from a mixture, or decide whether a model’s correct answer was genuinely audio-grounded. This suggests that the unifying concept is not a single algorithmic family, but a recurring decision problem: preserve, suppress, reweight, or reject audio-derived contributions according to task-specific relevance.

Formulation	What is filtered	Representative papers
Query-level gating	Whether an audio query is allowed to trigger prediction	(Wang et al., 27 Apr 2026)
Sample-level curation	Which clips enter training as reliable supervision	(Hai et al., 23 Sep 2025, Zhu et al., 2020, Giraldo et al., 2024)
Feature-level filtering	Which bands or time-frequency regions dominate representation	(Nam et al., 2021, Lai et al., 2018, Dawn et al., 2024)
Source/spatial filtering	Which source or channel contribution is retained	(Gfeller et al., 2020, Brendel et al., 2021, Das et al., 2020)
Context/attribution filtering	Which contributions are relevant under context, or whether audio mattered at all	(Luong et al., 21 May 2026, He et al., 25 Sep 2025, Morais et al., 25 Sep 2025)

2. Query-level gating and admissibility control

A particularly explicit formulation appears in ASR-SaSaSa2VA, where audio-contribution filtering is implemented as a binary no-target expression detection step for audio-guided video segmentation (Wang et al., 27 Apr 2026). The system has two distinct audio pathways. On the valid-query path, audio $\mathcal{A}$ is converted to text by Qwen3-ASR, specifically Qwen3-ASR-1.7B in experiments, via

$\mathcal{T} = \mathrm{ASR}(\mathcal{A}),$

and the transcript conditions the text-based referring video segmentation model SaSaSa2VA. On the filtering path, a LoRA-fine-tuned Qwen2.5-Omni model performs binary classification directly on raw audio and video, with output $y \in \{0,1\}$, where $y=0$ denotes a no-target expression and $y=1$ denotes a valid target query. The operational rule is hard gating: if $y=0$, segmentation is skipped; otherwise the ASR transcript is passed onward. No soft weighting, uncertainty state, or threshold calibration is reported.

The empirical effect is large. With Qwen3-ASR-1.7B and SaSaSa2VA-26B, removing no-target detection yields $\mathcal{J{data}F}=49.4$, N-acc $=7.6$, and T-acc $=97.3$; adding no-target detection gives $\mathcal{J{data}F}=63.9$, N-acc $\mathcal{T} = \mathrm{ASR}(\mathcal{A}),$0, and T-acc $\mathcal{T} = \mathrm{ASR}(\mathcal{A}),$1 (Wang et al., 27 Apr 2026). The pattern is archetypal for hard query filtering: false positives drop sharply, while a modest number of valid cases are also rejected. Just as importantly, the ASR ablation shows that improving transcription alone does not solve the admissibility problem: without explicit no-target detection, N-acc remains extremely poor.

This formulation clarifies a recurring misconception. Audio-to-text conversion answers what the query says; it does not answer whether the query should be allowed to affect prediction. Query-level audio-contribution filtering is therefore a validity decision, not merely a confidence boost for a downstream model.

3. Training-data curation and sample reliability

In synthetic-data pipelines, audio-contribution filtering often becomes a gate on whether a candidate clip should contribute supervision at all. SynSonic is exemplary: generated foreground events are scored by both CLAP similarity $\mathcal{T} = \mathrm{ASR}(\mathcal{A}),$2 and an AudioSet-based Dasheng-AS target-class logit $\mathcal{T} = \mathrm{ASR}(\mathcal{A}),$3, ranked independently within class, and fused by

$\mathcal{T} = \mathrm{ASR}(\mathcal{A}),$4

Samples are then sorted by $\mathcal{T} = \mathrm{ASR}(\mathcal{A}),$5 and only the top $\mathcal{T} = \mathrm{ASR}(\mathcal{A}),$6 are retained (Hai et al., 23 Sep 2025). The best setting is equal weighting $\mathcal{T} = \mathrm{ASR}(\mathcal{A}),$7 with top $\mathcal{T} = \mathrm{ASR}(\mathcal{A}),$8 retention. Under this setting, PSDS1/PSDS2 improve from $\mathcal{T} = \mathrm{ASR}(\mathcal{A}),$9 with no filtering to $y \in \{0,1\}$0. The paper’s central claim is that indiscriminately using generated audio provides little benefit, whereas joint score filtering with dual classifiers converts generative augmentation into genuinely useful training data.

A related problem is noisy human labeling rather than imperfect generation. CrossFilter treats a bi-quality dataset as curated subset $y \in \{0,1\}$1 plus noisy subset $y \in \{0,1\}$2, then uses two networks operating on different audio representations to cross-promote reliable examples from noisy to pseudo-curated pools (Zhu et al., 2020). The promotion rule is simple: if one network’s top prediction agrees with the given label, that sample is moved for the peer network, not for itself. This cross-update design is intended to reduce self-reinforcing selection bias. The method then trains curated and noisy subsets with different objectives, using a standard supervised loss on curated data and the robust $y \in \{0,1\}$3 loss on noisy data: $y \in \{0,1\}$4 On FSDKaggle2018, the combined system improves from $y \in \{0,1\}$5 mAP@3 without noise filtering to $y \in \{0,1\}$6 with it; on FSDKaggle2019, lwlrap rises from $y \in \{0,1\}$7 to $y \in \{0,1\}$8 (Zhu et al., 2020).

Crowdsourced speech curation yields a third variant. In Common Voice Catalan, the pipeline first enhances audio with VoiceFixer, then applies SoX spectral noise removal using a noise profile extracted from the final 0.5 seconds of the clip, trims silences longer than 0.1 seconds below $y \in \{0,1\}$9 dB, and finally filters clips by NISQA score (Giraldo et al., 2024). The retained subsets are defined by thresholds NISQA $y=0$0 for 800 hours, $y=0$1 for 200 hours, $y=0$2 for 100 hours, and $y=0$3 for 50 hours. When a diffusion-based TTS model is trained on these subsets, the 100-hour set performs best, with UTMOS $y=0$4, outperforming both the unenhanced matched-content baseline at $y=0$5 and the larger 800-hour filtered set at $y=0$6 (Giraldo et al., 2024). The resulting trade-off is clear: better quality filtering can outweigh greater data quantity.

4. Feature-, signal-, and content-level filtering

Some methods do not reject entire clips; instead they alter how different parts of the signal contribute inside the representation itself. FilterAugment operates on log-mel spectrograms by adding random gains in dB to multiple frequency bands, rather than deleting them as in frequency masking (Nam et al., 2021). In compact form,

$y=0$7

where $y=0$8 is either piecewise constant (step type) or piecewise linear (linear type). The linear variant is best in the reported sound event detection experiments, improving over baseline by 6.50%, whereas frequency masking improves by 2.13%; on speaker verification it achieves EER $y=0$9 against $y=1$0 for frequency masking (Nam et al., 2021). This is contribution filtering by relative spectral emphasis: some bands are attenuated, others strengthened, and the model is trained not to over-rely on a narrow frequency region.

"Attentive Filtering Networks for Audio Replay Attack Detection" (Lai et al., 2018) makes the same idea explicit at the time-frequency level. Given a log power magnitude spectrum map $y=1$1, the network learns an attention heatmap $y=1$2 and applies

$y=1$3

This is multiplicative reweighting with residual passthrough, not hard masking. The learned filter emphasizes replay-relevant regions in both time and frequency before a dilated residual classifier. The best single system, AF(Sigmoid)-DRN(ReLU), attains eval EER $y=1$4, compared with $y=1$5 for the same classifier without attentive filtering; the best fusion reaches $y=1$6, about 30% relative improvement over the enhanced CQCC-GMM baseline at $y=1$7 (Lai et al., 2018).

A more classical spectral-subtraction form appears in environmental denoising. The method of (Dias et al., 2022) rescales the waveform to $y=1$8, computes an STFT with Hamming window size 512 and hop 256, estimates a per-frequency background profile by histogram analysis with iterative outlier removal and Otsu thresholding, subtracts that profile, clips negatives to zero, and then applies a $y=1$9 local suppression rule. In the terrestrial example it removes more rain noise than the original Towsey filter, while the underwater case shows much smaller gains (Dias et al., 2022). Here the contribution to be removed is not “noise” in an abstract sense, but persistent bandwise background energy.

Temporal-content filtering can also be aggressive. In environmental sound classification, Audio Crop removes zero-valued samples, repeats the non-zero remainder to the corpus maximum duration, and then computes spectrograms for CNN classification (Dawn et al., 2024). The effect is to increase the proportion of informative event content seen by the classifier. On ESC-50, Audio Crop with EfficientNetB2 gives the best Level 1 accuracy at $y=0$0; by contrast, Band Stop filtering is consistently poor, which the paper interprets as evidence that the rejected $y=0$1–$y=0$2 Hz band carries much of the class-discriminative information (Dawn et al., 2024).

5. Source-, spatial-, and multidevice contribution isolation

At the source level, audio-contribution filtering becomes selective extraction of one contribution from a mixture. SoundFilter formulates this as one-shot conditional waveform separation: given a single-channel mixture $y=0$3 and a short reference waveform $y=0$4, a conditioning encoder produces an embedding that FiLM-modulates a waveform U-Net, yielding $y=0$5 (Gfeller et al., 2020). The model is trained end-to-end with SI-SDR loss, without source-class labels, on synthetic two-source mixtures. Reported SI-SDR improvement is $y=0$6 dB on FSD50k and $y=0$7 dB on Librispeech, with held-out-class performance of $y=0$8 dB, supporting the claim that the filter can be configured by exemplar rather than by a fixed ontology (Gfeller et al., 2020).

A different notion of contribution filtering appears in ad-hoc sensor networks, where multiple coded device streams are fused probabilistically rather than one source being extracted from a mixture. The postfilter of (Das et al., 2020) treats each decoded observation as interval-censored by quantization bin limits $y=0$9, forms a per-device truncated-Gaussian posterior over the clean coefficient, multiplies these posteriors across devices, and computes the MMSE estimate numerically with $\mathcal{J{data}F}=49.4$0 intervals. The central expression is

$\mathcal{J{data}F}=49.4$1

The method outperforms single-channel baselines and a multichannel Wiener filter in many tested conditions, showing that contribution fusion can benefit from modeling coding uncertainty directly rather than absorbing it into an additive Gaussian approximation (Das et al., 2020).

Spatial filtering relies on a related but more indirect object: the target’s spatial signature. "Manifold learning-supported estimation of relative transfer functions for spatial filtering" (Brendel et al., 2021) learns a VAE over broadband RTF vectors and then refines noisy estimates by projecting them toward a room-specific manifold or by solving a manifold-constrained least-squares problem. In some scenarios the hybrid LS estimator improves RTF SER by roughly 10 dB over the raw estimator (Brendel et al., 2021). "Particle Filtering on the Audio Localization Manifold" (Ettinger et al., 2010) performs an analogous operation on TDOA state vectors rather than waveforms: for a 7-microphone array, it tracks all $\mathcal{J{data}F}=49.4$2 pairwise delays with only 50 particles by projecting them onto a low-dimensional localization manifold learned through a PD-tree. In both cases, the filtered object is not the audio signal itself but the audio-derived spatial state used by downstream spatial filters.

6. Contextual and attribution-aware filtering

A more radical generalization is to define relevance contextually rather than absolutely. Automatic Contextual Audio Denoising (ACAD) restricts context to acoustic scene class and defines in-context (IC) events as events typical of the scene’s event distribution, while out-of-context (OC) events are scene-inconsistent additions (Luong et al., 21 May 2026). A pretrained CRNN scene classifier provides a context embedding $\mathcal{J{data}F}=49.4$3, and a FiLM-conditioned UNet denoiser predicts a mask: $\mathcal{J{data}F}=49.4$4 Jointly fine-tuning the context extractor and denoiser gives the best result: SI-SDR rises from $\mathcal{J{data}F}=49.4$5 for a context-free UNet to $\mathcal{J{data}F}=49.4$6, and SDR from $\mathcal{J{data}F}=49.4$7 to $\mathcal{J{data}F}=49.4$8 (Luong et al., 21 May 2026). The constant-conditioning controls do not help, which indicates that useful context, not conditioning machinery by itself, drives the improvement.

Attribution-oriented work asks a different question: not which signal component should be removed, but whether audio contributed to a decision at all. "Investigating Modality Contribution in Audio LLMs for Music" (Morais et al., 25 Sep 2025) adapts MM-SHAP to audio-text models by masking text tokens with [MASK], masking raw audio windows with zeros, estimating per-feature Shapley values $\mathcal{J{data}F}=49.4$9, and aggregating them into modality totals

$=7.6$0

On MuChoMusic, MU-LLaMA attains A-SHAP $=7.6$1 with accuracy $=7.6$2–$=7.6$3, whereas Qwen-Audio-Chat attains A-SHAP $=7.6$4–$=7.6$5 with higher accuracy $=7.6$6–$=7.6$7 (Morais et al., 25 Sep 2025). The paper’s main caution is that high benchmark accuracy does not imply high audio reliance, although local token-level analyses still show meaningful audio grounding in some cases.

"Measuring Audio's Impact on Correctness: Audio-Contribution-Aware Post-Training of Large Audio LLMs" (He et al., 25 Sep 2025) turns that diagnosis into a data-partitioning procedure. For each audio MCQ sample, the real audio is replaced by 30 seconds of silence and three models—Audio-Flamingo2, R1-AQA, and Kimi-Audio—answer the question. A sample is labeled weak audio-contribution if at least two of the three remain correct under silence: $=7.6$8 On AudioMCQ, the resulting split is 54.8% weak and 45.2% strong. This split then supports stage-specific post-training: Weak-to-Strong achieves $=7.6$9 on MMAU-test-mini and $=97.3$0 on MMAU, while Mixed-to-Strong achieves $=97.3$1 on MMAR and $=97.3$2 on MMSU (He et al., 25 Sep 2025). The general lesson is that training data can be filtered not only by quality but by the degree to which correctness genuinely depends on audio.

7. Trade-offs, misconceptions, and unresolved questions

One recurrent misconception is that better front-end recognition or larger models automatically solve contribution filtering. The ASR-SaSaSa2VA ablation contradicts this directly: improving ASR changes the valid-target path only slightly, while N-acc remains extremely poor without an explicit no-target gate (Wang et al., 27 Apr 2026). A plausible implication is that audio understanding pipelines must often separate semantic decoding from admissibility control.

A second misconception is that more data should always be retained. The opposite appears repeatedly. SynSonic shows that no filtering is much worse than top-50% retention, but top-25% is also worse because diversity is lost (Hai et al., 23 Sep 2025). Common Voice Catalan shows that 100 filtered hours outperform 800 filtered hours for downstream TTS (Giraldo et al., 2024). This suggests that contribution filtering is often governed by a quality–diversity or quality–quantity frontier rather than by monotone data accumulation.

A third misconception is that conditioning by any auxiliary variable is sufficient. ACAD’s constant-vector controls slightly underperform the plain context-free UNet, showing that conditioning channels alone do not create contextual competence (Luong et al., 21 May 2026). Likewise, low global audio attribution in Audio LLMs does not imply that audio is ignored everywhere; MM-SHAP reveals cases of sparse but well-localized token-level audio grounding (Morais et al., 25 Sep 2025). Contribution filtering therefore cannot be reduced to a single scalar confidence score without losing task-specific structure.

The unresolved problems are correspondingly broad. Many methods are domain-bound: room-specific RTF manifolds depend on enclosure stability (Brendel et al., 2021), scene-conditioned denoising depends on how context is defined (Luong et al., 21 May 2026), and model-based weak/strong splits depend on the filtering committee used to produce them (He et al., 25 Sep 2025). Several papers also leave calibration open: hard binary gates dominate, while soft reliability estimation, threshold tuning, and uncertainty handling are often absent. The literature therefore supports a precise but narrow conclusion: audio-contribution filtering is already a central design pattern across audio systems, but its implementation remains highly task-specific, and general-purpose criteria for deciding what audio should count, how strongly it should count, and when it should be ignored remain an active research problem.