Transfer Learning with Semi-Supervised Dataset Annotation for Birdcall Classification

We present working notes on transfer learning with semi-supervised dataset annotation for the BirdCLEF 2023 competition, focused on identifying African bird species in recorded soundscapes. Our approach utilizes existing off-the-shelf models, BirdNET and MixIT, to address representation and labeling challenges in the competition. We explore the embedding space learned by BirdNET and propose a process to derive an annotated dataset for supervised learning. Our experiments involve various models and feature engineering approaches to maximize performance on the competition leaderboard. The results demonstrate the effectiveness of our approach in classifying bird species and highlight the potential of transfer learning and semi-supervised dataset annotation in similar tasks.Comment: BirdCLEF working note submission to Multimedia Retrieval in Nature (LifeCLEF) for CLEF 202

Motif Mining and Unsupervised Representation Learning for BirdCLEF 2022

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.Comment: Submitted to CEUR-WS under LifeCLEF for the BirdCLEF 2022 challenge as a working not

Transcribed enhancers lead waves of coordinated transcription in transitioning mammalian cells

While it is generally accepted that cellular differentiation requires changes to transcriptional networks, dynamic regulation of promoters and enhancers at specific sets of genes has not been previously studied en masse. Exploiting the fact that active promoters and enhancers are transcribed, we simultaneously measured their activity in 19 human and 14 mouse time courses covering a wide range of cell types and biological stimuli. Enhancer RNAs, then mRNAs encoding transcription factors dominated the earliest responses. Binding sites for key lineage transcription factors were simultaneously over-represented in enhancers and promoters active in each cellular system. Our data support a highly generalizable model in which enhancer transcription is the earliest event in successive waves of transcriptional change during cellular differentiation or activation