Automated Acoustics Species Id: Automated Training Data
Channel: UC San Diego Engineers for Exploration
Date: 2022-09-29

Environment soundscapes hold rich information for measuring the biodiversity of many small, noisy species missed by traditional monitoring techniques. However, the effort needed to manually label thousands of hours of data at a species level for bio-analysis makes this infeasible. One could automate the labeling process with machine learning, but training a well-performing classifier requires labeled data. This summer, undergraduate students in the Automated Acoustics Species Identification Team in the 2022 E4E REU at the University of California, San Diego experimented with using pre-existing binary bird detectors to convert weakly labeled data (where a species is known to be present) into strongly labeled data (where the exact time of vocalizations is known) to train CNN/RNN hybrids. This proof of concept shows these “weakly to strongly labeled” pipelines can produce training data with few false positives and models with decent performance.

External Sources documented at https://docs.google.com/document/d/e/2PACX-1vRYdi7a0YDCbmSMaRtorwgCsEIK-526qliJaYltAlMVDq0G36Ov5YCrmIyTOOp1ebf_Z9K1xZD1MQUC/pub

This video released under CC BY-NC-SA 4.0.