SEAbird

Dataset creation and validation pipeline for Southeast Asian bird audio classification.

Dataset: The SEAbird dataset (6000 annotated 3-second segments, 10 species) is available on Zenodo:

Overview

End-to-end pipeline from Xeno-Canto downloads to optimized train/val/test splits:

1. Download recordings from Xeno-Canto (FLAC format)
2. Annotate bird vocalizations with interactive GUI
3. Extract 3-second segments
4. Quality control and filtering
5. Split with MIP optimization (prevents data leakage)

Pipeline Stages

Stage	Script	Description
1	Stage1_xc_fetch_metadata.py	Fetch recording metadata
2	Stage2_xc_dload_all_from_species_list.py	Download recordings
3	Stage3_xc_dload_delta_by_id.py	Download specific IDs
4	Stage4_eda_downloads.py	Exploratory data analysis
5	Stage5_find_segments_interactive.py	Interactive annotation GUI
6	Stage6_extract_annotated_segments.py	Extract WAV segments
7	Stage7_quality_control_selection.py	Quality control
8a	Stage8a_splitter_mip.py	MIP-based splitting (recommended)
8b	Stage8b_splitter_genetic_algorithm.py	GA-based splitting
8c	Stage8c_splitter_simulated_annealing.py	SA-based splitting
9	Stage9_train_seabird_multifeature.py	Train 4 CNN models

MIP Splitter (Stage 8a)

Generates CSV-based splits with configurable ratios:

python Stage8a_splitter_mip.py /path/to/dataset \
    --train_ratio 0.80 --val_ratio 0.10 --test_ratio 0.10 \
    --output /path/to/splits.csv

Output format:

# split_ratio=80:10:10 seed=42 objective=0 solver=mip_cbc
filename,split
xc1002657_2860.wav,test
xc1003831_2642.wav,train
...

Key features:

• Source-based separation (same recording never in multiple splits)
• Perfect class balance (objective=0 means exact ratios achieved)
• Reproducible via seed parameter
• CSV output for use with any training framework

Pre-generated Splits

Ready-to-use splits for 6000-sample dataset (seed=42, all objective=0):

splits_csv/
  seabird_splits_70_15_15_seed42.csv
  seabird_splits_75_10_15_seed42.csv
  seabird_splits_80_10_10_seed42.csv

Training

For audio-focused CNN training, see mun3im/mynanet.

Installation

pip install numpy scipy librosa soundfile requests tqdm matplotlib sounddevice pulp

License

MIT