Sound Event Detection

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9 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

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SEABAD: A Tropical Bird Activity Detection Dataset for Passive Acoustic Monitoring
May 20, 2026Muhammad Mun'im Ahmad Zabidi, Mohd Yamani Idna Idris, Norisma IdrisTraining Data Curation Sound Event Detection
Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia
A strongly annotated passive acoustic dataset for tropical bird monitoring
May 20, 2026Daniela Ruiz, Juan Sebastián Ulloa, Zhongqi Miao +11Sound Event Detection Bioacoustics
Microsoft AI for Good Research Lab, Redmond, Washington, United States · Universidad de Los Andes, Bogotá, Colombia, Center for Research and Formation in Artificial Intelligence · Instituto de Investigación de Recursos Biológicos Alexander von Humboldt, Bogotá, Colombia +2
Executable Boundary Contracts for Sound Event Traces
May 19, 2026Faruk Alpay, Hamdi AlakkadSound Event Detection
Department of Computer Engineering Bahcesehir University, Istanbul, Turkey · Department of Artificial Intelligence Engineering2026 Bahcesehir University, Istanbul, Turkey
FSD50K-Solo: Automated Curation of Single-Source Sound Events
May 13, 2026Ningyuan Yang, Sile Yin, Li-Chia Yang +4Training Data Curation Audio Understanding
Electrical & Computer Engineering Stony Brook University Stony Brook, NY, USA · Research Bose Corporation Framingham, MA, USA
NAACA: Training-Free NeuroAuditory Attentive Cognitive Architecture with Oscillatory Working Memory for Salience-Driven Attention Gating
May 13, 2026Zhongju Yuan, Geraint Wiggins, Dick BotteldoorenAudio Understanding Audio-Language Models
1WAVES Research Group, Ghent University, Gent, Belgium · 2AI Lab, Vrije Universiteit Brussel, Brussel, Belgium · 3EECS, Queen Mary University of London, London, UK
STRUM: A Spectral Transcription and Rhythm Understanding Model for End-to-End Generation of Playable Rhythm-Game Charts
May 12, 2026Joshua OpriaAudio Understanding Automatic Music Transcription
Independent Researcher
Towards Open World Sound Event Detection
May 5, 2026P. H. Hai, L. T. Minh, L. H. SonContinual Learning Detection Transformer
VNU University of Engineering and Technology, Hanoi, 100000, Vietnam · Artificial Intelligence Research Center, VNU Information Technology Institute, Hanoi, 100000, Vietnam
Smart Passive Acoustic Monitoring: Embedding a Classifier on AudioMoth Microcontroller
May 5, 2026Louis Lerbourg, Paul Peyret, Juliette Linossier +1Efficient Neural Network Inference Sound Event Detection
Univ. Grenoble Alpes, CEA, List, Grenoble, France · Biophonia, France
MMAudio-LABEL: Audio Event Labeling via Audio Generation for Silent Video
May 1, 2026Kazuya Tateishi, Akira Takahashi, Atsuo Hiroe +3Audio-Video Generation Sound Event Detection
Sony Group Corporation, Japan · Sony AI, USA
Detecting and measuring respiratory events in horses during exercise with a microphone: deep learning vs. standard signal processing
Aug 4, 2025Jeanne I. M. Parmentier, Rhana M. Aarts, Elin Hernlund +2Temporal Convolutional Networks Sound Event Detection
Utrecht University · University of Twente · Inertia Technology B.V +5
Binaural Sound Event Localization and Detection based on HRTF Cues for Humanoid Robots
Jul 28, 2025Gyeong-Tae Lee, Hyeonuk Nam, Yong-Hwa ParkDirection-of-Arrival Estimation Sound Event Detection
Korea Advanced Institute of Science and Technology, Daejeon, South Korea

May 20, 2026, cs.SDJ/K move · Enter open · S save

SEABAD: A Tropical Bird Activity Detection Dataset for Passive Acoustic Monitoring

Muhammad Mun'im Ahmad Zabidi, Mohd Yamani Idna Idris, Norisma Idris

Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia

Training Data Curation Sound Event Detection Bioacoustics Passive Acoustic Monitoring Wildlife Monitoring

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Passive acoustic monitoring (PAM) enables large-scale biodiversity assessment, but continuous recording generates large amounts of non-informative audio, creating challenges for storage, power consumption, and long-term edge deployment. Bird audio detection (BAD), which identifies bird vocalizations, can reduce this burden by filtering irrelevant recordings before downstream analysis. However, most BAD systems are trained on temperate datasets despite tropical soundscapes being denser, more species-rich, and acoustically unpredictable. To address this gap, we introduce SEABAD (Southeast Asian Bird Activity Detection), a dataset of 50,000 curated three-second clips from Southeast Asian soundscapes, evenly balanced between bird-present and bird-absent samples. The dataset spans 1,677 bird species and is standardized to 16 kHz mono audio for embedded and low-power inference. We developed a dual-branch curation pipeline: a six-stage positive-label workflow applied to Xeno-Canto recordings, alongside six source-specific negative-label extractions from environmental datasets. These procedures reduced class imbalance by 13.7% (Gini coefficient: 0.601 to 0.519). A manual audit of 1,000 positive clips confirmed 97.8% +/- 0.9% labeling accuracy. Baseline experiments using MobileNetV3-Small achieved 99.57% +/- 0.25% accuracy and 0.9985 +/- 0.0002 AUC across three random seeds. SEABAD and the full curation pipeline are publicly released to support tropical BAD research and energy-efficient acoustic monitoring.

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