10.5281/zenodo.6027024
https://zenodo.org/records/6027024
oai:zenodo.org:6027024
Colin A. Quinn
Colin A. Quinn
0000-0001-7813-690X
Patrick Burns
Patrick Burns
0000-0001-7971-4177
Gurman Gill
Gurman Gill
0000-0001-5827-9444
Shrishail Baligar
Shrishail Baligar
0000-0002-4385-6939
Rose L. Snyder
Rose L. Snyder
0000-0002-5487-4518
Leonardo Salas
Leonardo Salas
0000-0002-8801-548X
Scott J. Goetz
Scott J. Goetz
0000-0002-6326-4308
Matthew L. Clark
Matthew L. Clark
0000-0001-5953-2990
Convolutional neural network and data used for applied soundscape classification with Soundscapes 2 Landscapes (S2L)
Zenodo
2022
convolutional neural network
ecoacoustics
soundscape ecology
2022-02-09
eng
10.1016/j.ecolind.2022.108831
https://zenodo.org/record/6038460#.YlSw9erML-g
10.5281/zenodo.6027023
1.0.0
Creative Commons Attribution 4.0 International
This repository documents the ABGQI-CNN manuscript (DOI: https://doi.org/10.1016/j.ecolind.2022.108831). It contains supplementary materials, data used to train a soundscape classification convolutional neural network (CNN), and data to generate manuscript results. The accompanying code can be found at https://doi.org/10.5281/zenodo.6038459. Files include:
ABGQI-CNN.tar: saved CNN model weights for the 5-class soundscape classifier using a MobileNetV2 architecture pre-trained with bird vocalization data.
ABGQI_mel_spectrograms.tar: spectrograms used for fine-tuning the pre-trained CNN, above, with training, validation, and testing data splits.
freesound_licensing.csv: file names and license information related to Freesound auxiliary files.
RavenLite_Training_Data_Collection.pdf: a manual for RavenLite ROI annotation.
S2L_site_geog-env_data.csv: environmental and geographic data (sans GPS) related to site locations in S2L project 2017-2020.
site_avg_ABGQIU_fscore_075_daytime.csv: the average site rate of soundscape components for 5 a.m. to 8 p.m.
site_by_hour_ABGQIU_fscore_075.csv: the average hourly site rate of soundscape components
site_classifications_beta075.tar: a directory containing a CSV for every site with threshold optimized classifications for each 2-s Mel spectrogram
site_prediction_probabilies.tar: a directory containing a CSV for every site with ABGQI-CNN probabilities for each 2-s Mel spectrogram
Supplementary_Materials.pdf: includes additional material and analyses related to the accompanying manuscript.
Contact Colin Quinn at cq73@nau.edu for questions related to this repository or if you have an interest in the original wav recordings. Please be aware that underlying software, specifically for the CNN implementation, may not continue stability as python libraries are updated.