Motif Mining and Unsupervised Representation Learning for BirdCLEF 2022
arXiv:2206.04805v14 citationsh-index: 4Has Code
Originality Synthesis-oriented
AI Analysis
This addresses bird species identification for ecological monitoring, but it is incremental as it applies existing unsupervised techniques to a specific competition dataset.
The paper tackled bird species classification from audio by building a model using unsupervised representation learning with triplet loss on spectrogram motifs, achieving a score of 0.48 on the BirdCLEF 2022 public leaderboard.
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.