  Efficient and accurate bird sound classification is of important for ecology,
habitat protection and scientific research, as it plays a central role in
monitoring the distribution and abundance of species. However, prevailing
methods typically demand extensively labeled audio datasets and have highly
customized frameworks, imposing substantial computational and annotation loads.
In this study, we present an efficient and general framework called SSL-Net,
which combines spectral and learned features to identify different bird sounds.
Encouraging empirical results gleaned from a standard field-collected bird
audio dataset validate the efficacy of our method in extracting features
efficiently and achieving heightened performance in bird sound classification,
even when working with limited sample sizes. Furthermore, we present three
feature fusion strategies, aiding engineers and researchers in their selection
through quantitative analysis.
