This article addresses the automatic detection of vocal, nocturnally migrating birds from a
network of acoustic sensors. Thus far, owing to the lack of annotated continuous recordings …

Frog population has been experiencing rapid decreases worldwide, which is regarded as
one of the most critical threats to the global biodiversity. Therefore, large volumes of frog …

This paper focuses on the problem of bird species identification using audio recordings.
Following recent developments in deep learning, we propose a multi-layer alternating …

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

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 …

This paper proposes a multi-layer alternating sparse− dense framework for bird species
identification. The framework takes audio recordings of bird vocalizations and produces …

In this paper, a supervised algorithm is proposed for the identification and segmentation of
bird calls with K-means clustering using features learnt by matrix factorization. Singular …

We propose a supervised, convex representation based audio hashing framework for bird
species classification. The proposed framework utilizes archetypal analysis, a matrix …

This paper proposes a data-efficient, semi-supervised, two-pass framework for segmenting
bird vocalizations. The framework utilizes a binary classification model to categorize frames …

In this paper, we extend an existing algorithm for the segmentation of bird calls from the
background. By utilizing features which has information about the magnitude and phase of …