arXiv:2606. 08898v1 Announce Type: cross Abstract: In the task of few-shot class-incremental audio classification, the number of classes is assumed to always increase without considering the possibility of decrease.
arXiv:2607. 01297v1 Announce Type: cross Abstract: Most existing audio classification methods suppose that each query (testing) sample belongs to a class of support (training) samples, and misrecognize samples of unseen classes as seen classes (cannot reject samples of unseen classes).
arXiv:2609.17076v1 Announce Type: new Abstract: Few-shot audio classifiers may rely on foreground-background co-occurrences and fail when those correlations shift. On SpurAudio, the resulting represe...
arXiv:2605. 13075v3 Announce Type: replace-cross Abstract: Few-shot spoken word classification has largely been developed for applications where a small number of classes is considered, and so the potential of larger-scale few-shot spoken word classification remains untapped.
arXiv:2606. 31587v1 Announce Type: cross Abstract: Audio-Language Models (ALMs) achieve strong zero-shot performance by aligning audio with textual class descriptions.
arXiv:2605. 13672v1 Announce Type: cross Abstract: Few-shot classification (FSC) is widely used for learning from limited labeled data, yet most evaluations implicitly assume that target concepts are independent of contextual cues.
arXiv:2607. 26607v1 Announce Type: cross Abstract: Few-shot Open-set audio classification requires classifying query samples from known classes with a few labeled support samples while rejecting query samples from unknown classes.
Fully few-shot class-incremental audio classification (FFCAC) requires recognizing new sound classes from only a handful of labeled examples per session, without forgetting previously learned classes...
arXiv:2606. 15751v1 Announce Type: cross Abstract: Audio-Language Models (ALMs) have shown remarkable success in zero-shot audio classification by aligning audio waveforms with text.
arXiv:2307.12226v3 Announce Type: replace-cross Abstract: Machine learning models -- including prominent zero-shot models -- are often trained on datasets whose labels are only a small proportion of...
The paper explores methods to mitigate catastrophic forgetting in incremental learning for sound event classification. It evaluates architectural and regularization strategies on FSD50K and AudioSet, finding that deeper layers, especially the classifier head, are most vulnerable. The most effective approach identified is fully freezing the feature extractor while fine‑tuning a dynamic head, which achieves minimal forgetting, stable training, and a balanced trade‑off between memory stability and learning plasticity.
The paper introduces a domain‑specific parameter‑isolation architecture for domain‑incremental learning (DIL) in audio classification, aiming to preserve knowledge from earlier domains without accessing their data. By employing data‑free generative replay and cross‑domain feature generation, the method constructs new experts conditioned on all previously frozen models, thereby mitigating catastrophic forgetting. Applied to the DCASE 2026 Challenge Task 7, the approach achieves micro and macro accuracies of 78.4 % and 78.9 %, outperforming the baseline by 33 and 25 percentage points, respectively, with ablation studies confirming the contribution of each component.
arXiv:2412. 03771v4 Announce Type: replace-cross Abstract: Zero-shot learning enables models to generalise to unseen classes using semantic information, bridging the gap between training classes and previously unseen test classes.