Exploring Meta Information for Audio-based Zero-shot Bird Classification
Alexander Gebhard Andreas Triantafyllopoulos Teresa Bez Lukas Christ Alexander Kathan Björn W. Schuller
Advances in passive acoustic monitoring and machine learning have led to the
procurement of vast datasets for computational bioacoustic research.
Nevertheless, data scarcity is still an issue for rare and underrepresented
species. This study investigates how meta-information can improve zero-shot
audio classification, utilising bird species as an example case study due to
the availability of rich and diverse metadata. We investigate three different
sources of metadata: textual bird sound descriptions encoded via (S)BERT,
functional traits (AVONET), and bird life-history (BLH) characteristics. As
audio features, we extract audio spectrogram transformer (AST) embeddings and
project them to the dimension of the auxiliary information by adopting a single
linear layer. Then, we employ the dot product as compatibility function and a
standard zero-shot learning ranking hinge loss to determine the correct class.
The best results are achieved by concatenating the AVONET and BLH features
attaining a mean F1-score of .233 over five different test sets with 8 to 10
classes.