CoarseSoundNet: Building a reliable model for ecological soundscape analysis
Read the original on arXiv Machine Learning →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.
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