Sound Event Detection

Sound event detection (SED) aims to recognize acoustic events—such as speech, machinery, animal calls, or household sounds—and often locate them in time, sometimes also estimating their direction. Common approaches include CNNs, recurrent networks, and transformers trained with frame-level labels, alongside weakly supervised, semi-supervised, and self-supervised methods that reduce reliance on detailed annotation. Benchmarks such as DESED, AudioSet, and URBAN-SED support comparisons, while real-world bioacoustic, industrial, and domestic recordings test performance under overlapping sounds and changing environments. Key challenges are reliable temporal boundaries, limited labels and domain shift, and balancing accuracy with open-vocabulary capability and low-cost deployment.