arXiv AI July 16, 2026 By Shiqi Zhang, Tuomas Virtanen

Cover First, Disagree Softly: Rethinking Mismatch-First Active Learning for Frame-Level Audio Classification

arXiv:2607. 13571v1 Announce Type: cross Abstract: Sound event detection relies on frame-level strong labels whose annotation is expensive.

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 AI

Jul 8

TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios

arXiv:2607. 06179v1 Announce Type: cross Abstract: There are some datasets of varying scales for audio classification (AC) applied to different tasks.

By Hong Lyu, Mingru Yang, Qianhua He, Yanxiong Li, Jinxin Huang, Zhengyu Pei

arXiv AI

Jul 16

Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning

arXiv:2607. 13555v1 Announce Type: cross Abstract: Bioacoustic call-type classification relies on costly expert annotation.

By Shiqi Zhang, Marius Fai{\ss}, Ariana Strandburg-Peshkin, Tuomas Virtanen

rag

arXiv AI

3d ago

Active Learning with Imperfect Labels: Optimal Labeler Assignment and Sample Selection

arXiv:2512.12870v2 Announce Type: replace-cross Abstract: Active Learning (AL) is commonly used in applications where labeling data is expensive or time-consuming. In practice, however, labels are of...

By Pouya Ahadi, Blair Winograd, Camille Zaug, Karunesh Arora, Lijun Wang, Kamran Paynabar

benchmarks

arXiv Computer Vision

Sep 1

Acoustically Grounded Cost Learning for Open-Vocabulary Audio-Visual Semantic Segmentation

arXiv:2608.29121v1 Announce Type: new Abstract: Open-Vocabulary Audio-Visual Semantic Segmentation (OV-AVSS) aims to perform pixel-level segmentation of sound-emitting objects from an open set of cat...

By Tianrui Hui, Shaofei Huang, Qisong Han, Yaxiong Wang, Lechao Cheng, Zhedong Zheng, Zhun Zhong, Richang Hong, Meng Wang

computer-vision multimodal benchmarks

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

Jun 30

Semi-Supervised Sound Event Detection with Conditional Mixup and Embedding-Level Contrastive Loss

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.

By Nian Shao, Xian Li, Xiaofei Li

rag fine-tuning

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Active Learning with Imperfect Labels: Optimal Labeler Assignment and Sample Selection

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