cs.SD · 2026-09-15 · No. 114

Sound, 2026-09-15.

3 new papers in cs.SD. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

3 entries
  1. 01

    MAST: Label-Efficient, Robust, and Generalizable Sound Detection for Biodiversity Monitoring via Masked Audio Pretraining and Self-Training

    Tianyi Xu, Daniel Pimentel-Alarcón, Zuzana Buřivalová, Claudia Solís-Lemus

    cs.SD · cs.LG

    Passive acoustic monitoring can measure biodiversity at larger scales, but time--frequency annotation of animal vocalizations is expensive, site-specific, and difficult to sustain at scale. We present a label-efficient sound detection framework that combines masked audio pretraining with a lightweight detector on mel spectrograms, then further improves robustness through iterative self-training on unlabeled audio. We first pretrain a...

    arxiv.org/abs/2609.15221 · PDF

  2. 02

    Augmenting Large Audio-Language Models with Frame-Level Grounding for Fine-Grained Temporal Perception

    Yanfeng Shi, Yan Song, Junhui Li, Tinggan Huang, Wu Guo, Haoyu Song, Ian McLoughlin

    cs.SD · cs.AI

    Large Audio-Language Models (LALMs) have substantially advanced general audio understanding, yet they remain limited in fine-grained temporal perception, particularly in precise event localization. Existing approaches primarily post-train LALMs to predict event boundaries as timestamp tokens. However, this generative formulation lacks explicit correspondence between the timestamp predictions and fine-grained acoustic evidence, limiting the...

    arxiv.org/abs/2609.15215 · PDF

  3. 03

    Rethinking Procedural Audio Pre-training: Source Scaling and Objective Adaptation

    Jiajun Peng, Fengrui Liu, Xinyu Liu, Feng Liu

    cs.SD · cs.AI

    Procedural audio has emerged as a viable source for transferable audio representation learning, but its design principles remain unclear.We revisit two questions: how a procedural source should be scaled, and whether training choices developed on natural audio should transfer unchanged to procedural data.Using a controlled source, we separate scale into formula-class coverage C and within-class rendering diversity I.Experiments with FDSL and...

    arxiv.org/abs/2609.15067 · PDF

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