Motif Mining and Unsupervised Representation Learning for BirdCLEF 2022
arXiv:2206.04805
Abstract
We build a classification model for the BirdCLEF 2022 challenge using unsupervised methods. We implement an unsupervised representation of the training dataset using a triplet loss on spectrogram representation of audio motifs. Our best model performs with a score of 0.48 on the public leaderboard.
Submitted to CEUR-WS under LifeCLEF for the BirdCLEF 2022 challenge as a working note