arXiv AI June 9, 2026 By Yanxiong Li, Guoqing Chen, Qianqian Li, Sen Huang

Few-shot Class-variable Incremental Audio Classification via Prototype Adaptation and Pseudo Class-variable Training

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Jul 3

Few-Shot Open-Set Audio Classification Using Attention Information-Fused Prototypes

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).

By Yanxiong Li, Jiaxin Tan, Qianqian Li, Guoqing Chen, Sen Huang, Tuomas Virtanen

rag

arXiv AI

Sep 16

Sample-Conditioned Representation Selection for Audio Few-Shot Learning

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...

By Fengrui Liu, Ningxin Shen, Yi Li, Yiwei Fu, Feng Liu, Jiangmeng Li

arXiv AI

Jun 6

Scaling few-shot spoken word classification with generative meta-continual learning

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.

By Louise Beyers, Batsirayi Mupamhi Ziki, Ruan van der Merwe

arXiv AI

Jul 1

ZEBRA: Zero-Shot Entropy-Regularized Prompt Learning for Base-to-Novel Generalization in Audio-Language Models

arXiv:2606. 31587v1 Announce Type: cross Abstract: Audio-Language Models (ALMs) achieve strong zero-shot performance by aligning audio with textual class descriptions.

By Asif Hanif, Mohammad Yaqub

llms

arXiv AI

Jun 4

SpurAudio: A Benchmark for Studying Shortcut Learning in Few-Shot Audio Classification

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.

By Giries Abu Ayoub, Morad Tukan, Loay Mualem

computer-vision benchmarks

arXiv Machine Learning

Jul 30

Few-Shot Open-Set Audio Classification via Transductive Prototype Refinement and Class Logit Enhancement

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.

By Tianyan Deng, Yanxiong Li, Rui Gao, Jiahao Du

benchmarks

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Related stories

Few-Shot Open-Set Audio Classification Using Attention Information-Fused Prototypes

Sample-Conditioned Representation Selection for Audio Few-Shot Learning

Scaling few-shot spoken word classification with generative meta-continual learning

ZEBRA: Zero-Shot Entropy-Regularized Prompt Learning for Base-to-Novel Generalization in Audio-Language Models

SpurAudio: A Benchmark for Studying Shortcut Learning in Few-Shot Audio Classification

Few-Shot Open-Set Audio Classification via Transductive Prototype Refinement and Class Logit Enhancement