Machine Learning for Bird Song Learning (ML4BL) dataset

Zenodo (CERN European Organization for Nuclear Research)

Abstract

General description This dataset contains Zebra Finch decisions about perceptual similarity on song units. All the data and files are used for reproducing the results of the paper 'Bird song comparison using deep learning trained from avian perceptual judgments' by the same authors. Git repo on Zenodo: https://doi.org/10.5281/zenodo.5545932 Git repo access: https://github.com/veronicamorfi/ml4bl/tree/v1.0.0 Directory organisation: ML4BL_ZF |_files |_Final_probes_20200816.csv - all trials and decisions of the birds (aviary 1 cycle 1 data are removed from experiments) |_luscinia_triplets_filtered.csv - triplets to use for training |_mean_std_luscinia_pretraining.pckl - mean and std of luscinia triplets used for trianing |_*_cons_* - % side consistency on triplets (train/test) - train set contains both train and val splits |_*_gt_* - cycle accuracy for triplets of the specific bird (train/test) - train set contains both train and val splits |_*_trials_* - number of decisions made for a triplet (train/test) - train set contains both train and val splits |_*_triplets_* - triplet information (aviary_cycle-acc_birdID, POS, NEG, ANC) (train/test) - train set contains both train and val splits |_*_low*_ - low-margin (ambiguous) triplets (train/val/test) |_*_high_ - high-margin (unambiguous) triplets (train/val/test) |_*_cycle_bird_keys_* - unique aviary_cycle-acc_birdID keys (train/test) - train set contains both train and val splits |_TunedLusciniaV1e.csv - pairwise distance of two recordings computed by Luscinia |_training_setup_1_ordered_acc_single_cons_50_70_trials.pckl - dictionary containing everything needed for training the model (keys: 'train_keys', 'train_triplets', 'val_keys', 'vali_triplets', 'test_triplets', 'test_keys', 'train_mean', 'train_std') |_melspecs - *.pckl - melspectrograms of recordings |_wavs - *wav - recordings |_README.txt Recordings 887 syllables extracted from zebra finch song recordings, with a sampling rate of 48kHz and high pass filtered (100Hz), with a 20ms intro/outro fade. Decisions Triplets were created from the recordings and the birds made side based decisions about their similarity (see 'Bird song comparison using deep learning trained from avian perceptual judgments' for further information). Training dictionary Information Dictionary keys: 'train_keys', 'train_triplets', 'val_keys', 'vali_triplets', 'test_triplets', 'test_keys', 'train_mean', 'train_std' train_triplets/vali_triplets/test_triplets: Aviary_Cycle_birdID, POS, NEG, ANC, Decisions, Cycle_ACC(%), Consistency(%) train_keys/val_keys/test_keys: Aviary_Cycle_birdID train_mean/train_std: shape: (1, mel_bins) Open Access This dataset is available under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. Contact info Please send any questions about the recordings to: Lies Zandberg: Elisabeth.Zandberg@rhul.ac.uk Please send any feedback or questions about the code and the rest of the data to: Veronica Morfi: g.v.morfi@qmul.ac.uk

Authors 6

1. Lies Zandberg

   Queen Mary University of London · Royal Holloway University of London

   Affiliation as printed

   Royal Holloway University of London; Queen Mary University of London

2. Veronica Morfi

   Queen Mary University of London

   Affiliation as printed

   Queen Mary University of London

3. Julia M. George

   Queen Mary University of London · Clemson University

   Affiliation as printed

   Queen Mary University of London; Clemson University, Clemson

4. David F. Clayton

   Queen Mary University of London · Clemson University

   Affiliation as printed

   Queen Mary University of London; Clemson University, Clemson

5. Dan Stowell Aachen

   Naturalis Biodiversity Center · Queen Mary University of London · Tilburg University

   Affiliation as printed

   Queen Mary University of London; Tilburg University; Naturalis Biodiversity Centre

6. Robert F. Lachlan

   Queen Mary University of London · Royal Holloway University of London

   Affiliation as printed

   Royal Holloway University of London; Queen Mary University of London