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.
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: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: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:2606. 31587v1 Announce Type: cross Abstract: Audio-Language Models (ALMs) achieve strong zero-shot performance by aligning audio with textual class descriptions.
arXiv:2606. 29901v1 Announce Type: cross Abstract: Sound event detection (SED) is a core module for acoustic environmental analysis, yet its performance is often limited by scarce labeled data.
arXiv:2412. 03771v3 Announce Type: replace-cross Abstract: Zero-shot learning enables models to generalise to unseen classes by leveraging semantic information, bridging the gap between training and testing sets with non-overlapping classes.
arXiv:2511. 16757v2 Announce Type: replace-cross Abstract: Audio-language pretraining (ALP) holds promise for learning general-purpose audio representation, yet remains underexplored.
arXiv:2607. 03134v1 Announce Type: cross Abstract: Recent research expands beyond binary anti-spoofing with the emergence of Source Tracing, the task of identifying the specific generative origins of synthetic speech.
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.
arXiv:2512. 10120v2 Announce Type: replace-cross Abstract: General-purpose audio representations aim to map acoustically variable instances of the same event to nearby points, resolving content identity in a zero-shot setting.
The rapid advancement of synthetic speech generation methods has made audio deepfake detection a critical challenge in multimedia forensics. While recent approaches achieve high detection accuracy, they typically rely on black-box architectures that offer limited interpretability and high computational complexity.
arXiv:2607. 03806v1 Announce Type: cross Abstract: Audio foundation models are widely adopted as general-purpose feature extractors, yet the internal structure of their learned representations remains insufficiently understood.