arXiv Machine Learning By Christiaan M. Geldenhuys, Thomas R. Niesler

A Parameter-Free Few-Shot Evaluation for Elephant Vocalisation Classification

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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.

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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
arXiv Machine Learning
Sep 11

When do cheap embeddings beat protein language models? A theoretically-grounded hashing sketch for biological sequence classification

The paper introduces Murmur2Vec, a lightweight, alignment‑free embedding that uses k‑mer counts hashed with MurmurHash to create a compact representation for biological sequences. It provides a full theoretical analysis, including bias/variance formulas, a Johnson–Lindenstrauss‑style concentration bound, and an excess‑risk bound that clarifies the trade‑off between hash‑table size and classifier performance. Empirically, Murmur2Vec matches or surpasses a fine‑tuned 650M‑parameter ESM‑2 protein language model across several classification tasks, including SARS‑CoV‑2 spike lineage and HIV‑1 Env subtype identification.

By Sarwan Ali, Taslim Murad, Imdadullah Khan, Safi Faizullah