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Bioacoustic sound event detection (SED) is a critical field for biodiversity monitoring, yet the high cost of annotating data poses significant challenges. This thesis explores the application of active learning strategies to reduce the amount of annotated data required for effective model training. This is done for a segment based SED model, where batch active learning is performed by querying fu
