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 …

The paper address the task of identifying multiple bird species from audio recordings. The
proposed approach uses one of the pre-trained Deep Convolutional Neural Network …

Bird call recognition using deep neural network-hidden Markov model (DNN-HMM)-based
transcription is proposed. The work is an attempt to adapt the human speech recognition …

Birds play an extremely important role in an ecosystem, identifying bird species in audio
recordings is challenging and has high research value. This paper aims to develop an …

Birds are crucial to the ecosystem's optimum functioning since they maintain a stable
climate, convert toxins into nutrients, and oxygenate the atmosphere. Identifying multiple bird …

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 …

In this paper, we propose a deep learning framework which combines the generalizability of
Gaussian mixture models (GMM) and discriminative power of deep matrix factorization to …

Birds are excellent bioindicators, playing a vital role in maintaining the delicate balance of
ecosystems. Identifying species from bird vocalization is arduous but has high research …

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