SeqFusionNet: A hybrid model for sequence-aware and globally integrated acoustic representation.

ABSTRACT: Animals communicate information primarily via their calls, and directly using their vocalizations proves essential for executing species conservation and tracking biodiversity. Conventional visual approaches are frequently limited by distance and surroundings, while call-based monitoring concentrates solely on the animals themselves, proving more effective and straightforward than visual techniques. This paper introduces an animal sound classification model named SeqFusionNet, integrating the sequential encoding of Transformer with the global perception of MLP to achieve robust global feature extraction. Research involved compiling and organizing four common acoustic datasets (pig, bird, urbansound, and marine mammal), with extensive experiments exploring the applicability of vocal feature

SUBMITTER: Wu T 

PROVIDER: S-EPMC12416843 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature