Bioacoustic classification often suffers from the lack of labeled data. This hinders the
effective utilization of state-of-the-art deep learning models in bioacoustics. To overcome this …

In this study, we propose Signal Latent Subspace (SLS), a flexible method that classifies
environmental sound events using the subspace representations of latent features obtained …

We present the quantitative characterization of Grande Island's off-reef acoustic environment
within the Zuari estuary during the pre-monsoon period. Passive acoustic recordings reveal …

Clustering is a fundamental learning task with many applications in a wide range of fields.
Recently proposed techniques have shown that performing clustering in a discriminative …

We introduce a new classification framework that combines the characteristics of matrix
factorization with the discriminative capabilities of kernel methods. Short-time analysis of …

Bioacoustic signal classification is a powerful tool for biologists, assisting in tasks such as
environmental monitoring of biomes in areas of difficult access, and providing clues about …

In this paper, a deep convex matrix factorization framework is proposed for bioacoustics
classification. Archetypal analysis, a form of convex non-negative matrix factorization, is …

Automatic classification of bioacoustic signals is an essential tool in biology for laborious
tasks such as environmental monitoring in areas of difficult access. A working system …

Classifying bioacoustic signals is a fundamental task for ecological monitoring. However,
this task includes several challenges, such as nonuniform signal length, environmental …

Recently, studies on machine learning have focused on methods that use symmetry implicit
in a specific manifold as an inductive bias. Grassmann manifolds provide the ability to …