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
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.
By Yanxiong Li, Guoqing Chen, Qianqian Li, Sen Huang
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
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:2608. 14824v1 Announce Type: cross Abstract: We present a parameter-free episodic evaluation of nearest-centroid classification for elephant vocalisations on fixed pretrained acoustic embeddings, across the Elephant Voices (EV) and Linguistic Data Consortium (LDC) datasets.
By Christiaan M. Geldenhuys, Thomas R. Niesler
arXiv:2512.07571v3 Announce Type: replace
Abstract: This paper presents a simple method that allows to easily enhance textual pre-trained large language models with speech information, when fine-tune...
By Nicolas Calbucura, Jose Guillen, Valentin Barriere