arXiv:2606. 18135v1 Announce Type: cross Abstract: In this work, we introduce the Certus Caliber Classification Gunshot Dataset (C3GD), a publicly accessible data set developed for the analysis of firearm muzzle blast sounds.
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.
arXiv:2607. 16736v1 Announce Type: cross Abstract: This paper presents RealDESED, a real-world domestic sound event detection (SED) benchmark comprising 5,710 audio recordings collected by 652 participants in their homes.
SsgCaps is a publicly available dataset of human-engineered sound scenes, each paired with a precisely structured prompt that guides the sampling process. The prompts are drawn from a predefined action-based typology, enabling extensive yet plausible sampling. A comparative quantitative analysis shows only small differences between the open and private versions, supporting the recommendation of the open version for benchmarking sound scene generation algorithms.
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.
arXiv:2607. 06179v1 Announce Type: cross Abstract: There are some datasets of varying scales for audio classification (AC) applied to different tasks.
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.
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).
CAST-Bench is a new benchmark for multi-shot audio‑video generation that traces each generated sound back to its intended source. It contains 900 prompts across nine sound‑source categories, including both single‑shot and multi‑shot narratives, and evaluates models using 18 metrics grouped into Visual Performance, Content Consistency, and Audio & Semantic Alignment. The benchmark reveals that while visual quality is largely saturated, audio semantic scores vary significantly, and multi‑shot prompts expose challenges such as consistent speaker identity and cross‑shot event retention.
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...
The study investigates why machine‑learning models for tuberculosis screening based on cough acoustics fail to generalize across datasets. Classical ML and deep‑learning classifiers achieved moderate performance within their own datasets (ROC‑AUC up to 0.755) but performed poorly on external data, often below 0.6. The authors found that audio features were more influenced by recording device and dataset than by TB status, and that device‑diverse training improved transfer while device mismatch degraded it. A clinical‑variable baseline showed more consistent generalization, suggesting acquisition‑specific variability is a stronger driver of poor generalizability than population shift.
arXiv:2607. 25543v1 Announce Type: cross Abstract: Generative AI has rapidly expanded audio-visual forgery beyond human-centric deepfakes into general scenes.