  This technical report presents our frame-level embedding learning system for
the DCASE2024 challenge for few-shot bioacoustic event detection (Task 5).In
this work, we used log-mel and PCEN for feature extraction of the input audio,
Netmamba Encoder as the information interaction network, and adopted data
augmentation strategies to improve the generalizability of the trained model as
well as multiple post-processing methods. Our final system achieved an
F-measure score of 56.4%, securing the 2nd rank in the few-shot bioacoustic
event detection category of the Detection and Classification of Acoustic Scenes
and Events Challenge 2024.
