CoarseSoundNet is a deep learning framework designed to classify ecological soundscapes into biophony, geophony, and anthropophony under realistic passive acoustic monitoring conditions. The study systematically explores model architectures, training data composition, and evaluation strategies, finding that adding more domain‑similar data, a silence class, and post‑hoc calibration improves performance. An ecological case study shows that pre‑filtering recordings with CoarseSoundNet produces acoustic index trends similar to ground‑truth filtering, though the impact on avian alpha‑diversity relationships is modest.
By Alexander Gebhard, Andreas Triantafyllopoulos, Dominik Arend, Sandra M\"uller, Svenja Schmidt, Michael Scherer-Lorenzen, Bj\"orn W. Schuller
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:2607. 01974v1 Announce Type: cross Abstract: This technical report describes our system for Task 1 of the DCASE 2026 Challenge, which aims to classify heterogeneous audio recordings according to the Broad Sound Taxonomy (BST).
By Beile Ning, Jiayi Yu, Zitong Wang, Yufei Hu, Wenjun Xu, Yuanhang Qian, Zhongxin Bai, Gongping Huang
The paper introduces LAST, a Looped Audio Spectrogram Transformer that processes all tokens once and then reuses the same blocks to refine only the class token over fixed audio features, making subsequent passes inexpensive. On AudioSet, a ten‑pass LAST outperforms a twelve‑layer sequential transformer by 2.1% relative mean average precision while using 49.4% fewer parameters, 42% fewer MACs, and achieving 9.8% higher throughput. Increasing the pass count from two to ten improves accuracy with only a 1.2% increase in computation, and the model shows enhanced robustness to temporal masking and other auditory augmentations across music, environmental, and event sound classification tasks.
By Haider Al-Tahan, Sean O'Brien, Anastasia Razdaibiedina, N. Apurva Ratan Murty
arXiv:2606. 02341v1 Announce Type: cross Abstract: Underwater acoustic classification has a wide array of oceanic applications, but faces challenges due to an increasingly complex acoustic environment.
By Amirmohammad Mohammadi, Joshua Peeples, Alexandra Van Dine
arXiv:2608. 08207v1 Announce Type: cross Abstract: Multivariate Time Series Classification (MTSC) demands models that can effectively capture complex temporal patterns across multiple scales while remaining computationally efficient.
By Pingping Liu, Muyao Wang, Zijian Zhang, Tongshun Zhang, Hao Miao, Guorui Xie, Qingliang Li, Qiuzhan Zhou