arXiv AI By Sinclair Gurny, Ryan Quinn

Exploring Feature Extraction Technique Parameters for Acoustic Gunshot Classification

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arXiv:2606. 19568v1 Announce Type: cross Abstract: Acoustic gunshot detection is a problem with applications across civilian public safety, military operations, and wildlife conservation, yet the field lacks a rigorous exploration of feature extraction techniques with a focus on generalization to realistic data.

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arXiv Machine Learning
Sep 24

"What's That Sound?": A Versatile, Robust, and Lightweight Convolutional Transformer for Environment Sound Recognition

The paper introduces RALCT, a lightweight Convolutional Transformer that combines randomized audio augmentations, MFCCs, and log‑mel spectrograms to extract robust features for environmental sound recognition. With only about 310,000 parameters, RALCT achieves state‑of‑the‑art accuracy—over 93% on UrbanSound8K, peaking at 94.56%—making it suitable for deployment on mobile devices. The authors also develop a mobile app that integrates the model to provide real‑time safety alerts for hearing‑impaired users.

By Julia Huang
arXiv Machine Learning
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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