SEABAD: Southeast Asian Bird Audio Detection Dataset

A dataset of 50,000 automatically curated 3-second clips spanning 1,677 Southeast Asian bird species, standardized to 16 kHz mono for binary bird presence–absence detection on edge devices.

📦 Dataset: zenodo.org/records/18290494
💻 Code: github.com/mun3im/seabad
📄 Paper: SEABAD: A Tropical Bird Audio Detection Dataset for Passive Acoustic Monitoring (2026) arXiV 2605.20853.

Overview

Passive acoustic monitoring (PAM) enables large-scale biodiversity assessment, but most recordings contain non-informative audio. Bird audio detection (BAD)—determining bird presence without species classification—can suppress non-target recordings on-device, extending deployment and reducing annotation burden.

SEABAD addresses critical gaps in existing datasets:

✅ Tropical soundscape coverage (Southeast Asia)
✅ 3-second clip length matching edge-AI inference windows
✅ Diversity-aware species balancing (1,677 species, Gini 0.519)
✅ Multi-source negative curation (environmental + field recordings)

Property	Value
Total clips	50,000
Positive (bird present)	25,000
Negative (bird absent)	25,000
Unique bird species	1,677
Clip duration	3 seconds
Sample rate	16 kHz mono
Bit depth	16-bit PCM
Geography	Indonesia, Malaysia, Thailand, Singapore, Brunei
Train / Val / Test split	40k / 5k / 5k (80% / 10% / 10%)

🎯 Baseline Validation Results

Standard CNN architectures (ImageNet pre-trained, fine-tuned on SEABAD) averaged across 3 random seeds (42, 100, 786):

Model	Params	Accuracy	AUC	Precision	Recall	F1
MobileNetV3-Small†	1.1M	99.57 ± 0.25%	0.9985 ± 0.0002	0.9956 ± 0.0012	0.9957 ± 0.0008	0.9957 ± 0.0025
EfficientNetB0	4.4M	99.49 ± 0.23%	0.9991 ± 0.0004	0.9959 ± 0.0018	0.9939 ± 0.0051	0.9949 ± 0.0023
VGG16	14.9M	99.61 ± 0.03%	0.9995 ± 0.0001	0.9960 ± 0.0014	0.9963 ± 0.0010	0.9961 ± 0.0025
ResNet50	24.2M	99.73 ± 0.02%	0.9992 ± 0.0003	0.9965 ± 0.0013	0.9980 ± 0.0012	0.9973 ± 0.0019

†Primary baseline for edge deployment

Key Findings

✅ All models achieve >99.4% accuracy with minimal variance (std <0.25%)
✅ MobileNetV3-Small is only 0.16% behind ResNet50 but has 22× fewer parameters
✅ Excellent training stability confirmed across diverse architectures and random seeds
✅ High dataset quality validated by consistent performance

See validation/README.md for detailed experimental setup and analysis.

📂 Repository Structure

seabad/
├── positive-label-curation/   # Stages 1–9: Xeno-Canto bird clips
│   ├── Stage1_xc_fetch_bird_metadata.py
│   ├── Stage2_analyze_metadata.py
│   ├── Stage3_download_and_convert.py
│   ├── Stage4_deduplicate_flac.py
│   ├── Stage5_extract_wav_from_flac.py
│   ├── Stage6_balance_species.py
│   ├── Stage7_qa_spectrograms.py
│   ├── Stage8_adjust_onset.py
│   └── Stage9_qa_apply_corrections.py
│
├── negative-sample-curation/  # Stages 1–6: Non-bird clips
│   ├── Stage1_extract_birdvox.py
│   ├── Stage2_extract_freefield.py
│   ├── Stage3_extract_warblr.py
│   ├── Stage4_extract_fsc22.py
│   ├── Stage5_extract_esc50.py
│   └── Stage6_extract_datasec.py
│
└── validation/                # CNN baseline training & evaluation
    ├── validate_seabad_pretrained.py
    ├── run_all_cnn.sh
    ├── utils.py
    └── README.md

🔧 Curation Pipeline

Six-Stage Methodology

Metadata Acquisition — Xeno-Canto API query for Southeast Asian bird species
Sound Acquisition — Download + convert to 16 kHz mono FLAC
Acoustic Deduplication — FAISS approximate nearest-neighbor on mel embeddings
Segment Extraction — RMS-based sliding window with minimum separation
Species Balancing — Diversity-aware balancing using MiniBatch K-Means + salience ranking
Quality Assurance — Manual audit with interactive correction tool

Positive Curation Highlights

Acoustic deduplication: FAISS cosine similarity on mel-spectrogram embeddings
- 13 near-duplicates identified and removed (0.03% of 38,481 recordings)
Diversity-aware segment extraction:
- RMS sliding window with 1.5s minimum separation
- One representative clip per source recording
Species balancing:
- MiniBatch K-Means (5 clusters/species) + salience-ranked priority queue
- Gini coefficient reduced from 0.601 → 0.519 (13.7% reduction)
- Mean samples/species: 14.9 (preserves all 1,677 species)
Quality assurance:
- 1,000 clips manually audited (Cochran n=639 for 95% CI)
- 97.8% accuracy (22 corrections: 15 onset, 6 noise, 1 false positive)
- 92.1% rated quality A/B by Xeno-Canto community

Negative Curation Highlights

Sources totaling 25,000 bird-absent clips:

Dataset	Clips	Description
BirdVox-DCASE-20k	9,983	Northeast USA field recordings
Freefield1010	5,755	Global crowdsourced environmental audio
Warblr	1,950	UK field recordings
FSC-22	1,875	Forest sounds (mammals, insects, weather)
ESC-50	444	Urban/mechanical/human sounds (high RMS)
DataSEC	3,597	Mediterranean soundscapes

All clips:

Resampled to 16 kHz mono
Centered 3-second extraction
No normalization (preserves naturalistic amplitude distribution)
Avian classes excluded from general sound datasets

🚀 Quick Start

Prerequisites

# Python dependencies
pip install pandas requests librosa soundfile tqdm scikit-learn matplotlib faiss-cpu numpy tensorflow

# System dependencies
# macOS: brew install ffmpeg
# Linux: apt-get install ffmpeg

For GPU FAISS acceleration: replace faiss-cpu with faiss-gpu.

1️⃣ Run Positive Pipeline

cd positive-label-curation

# Stage 1: Fetch metadata from Xeno-Canto API (~10 min)
python Stage1_xc_fetch_bird_metadata.py --country all

# Stage 2: Analyze metadata (optional statistics)
python Stage2_analyze_metadata.py

# Stage 3: Download and convert to FLAC (~2-6 hours)
python Stage3_download_and_convert.py

# Stage 4: Deduplicate using FAISS (~30 min)
python Stage4_deduplicate_flac.py --quarantine-all

# Stage 5: Extract 3s clips from FLAC (~1 hour)
python Stage5_extract_wav_from_flac.py --no-quarantine

# Stage 6: Balance species distribution (~15 min)
python Stage6_balance_species.py

# Stage 7: Generate QA spectrograms for manual review
python Stage7_qa_spectrograms.py

# Stage 8: Interactive onset correction tool (optional)
python Stage8_adjust_onset.py

# Stage 9: Apply corrections from QA
python Stage9_qa_apply_corrections.py

2️⃣ Run Negative Pipeline

cd negative-sample-curation

# Extract from DCASE 2018 datasets
python Stage1_extract_birdvox.py
python Stage2_extract_freefield.py
python Stage3_extract_warblr.py

# Extract from environmental sound datasets
python Stage4_extract_fsc22.py
python Stage5_extract_esc50.py
python Stage6_extract_datasec.py

Note: Ensure source datasets are downloaded and paths are configured in each script.

3️⃣ Run Baseline Validation

cd validation

# Train single model
python validate_seabad_pretrained.py --model mobilenetv3s --seed 42

# Train all models across all seeds
./run_all_cnn.sh

# Train specific seeds only
./run_all_cnn.sh 42 100

Supported models: mobilenetv3s, resnet50, vgg16, efficientnetb0

See validation/README.md for detailed usage and configuration.

📦 Pre-Compiled Dataset (Zenodo)

Download the complete 50,000-clip dataset: zenodo.org/records/18290494

Includes:

✅ 50,000 × 3-second clips (WAV, 16 kHz mono, 16-bit PCM)
✅ Train / validation / test CSVs (80/10/10 stratified split)
✅ Full provenance metadata:
- Xeno-Canto recording IDs
- GPS coordinates
- Original licenses (CC BY, CC BY-NC-SA, etc.)
- Species taxonomy (IOC World Bird List)
- Quality ratings
- Source dataset attribution (for negative samples)

🎛️ Audio Processing Standards

All clips standardized to:

Parameter	Value	Rationale
Sample rate	16,000 Hz	AudioMoth default, Nyquist covers 0-8 kHz
Channels	Mono	Edge deployment memory constraint
Duration	3.0 seconds	BirdNET inference window
Format	WAV PCM_16	Lossless, hardware-compatible
Normalization	None	Preserves naturalistic amplitude for energy-based detection
Source format	FLAC PCM_16	Intermediate lossless storage

📊 Dataset Statistics

Geographic Distribution (Post-Balancing)

Country	Clips	Percentage
Indonesia	9,155	36.6%
Malaysia	8,400	33.6%
Thailand	5,996	24.0%
Singapore	1,388	5.6%
Brunei	61	0.2%

Species Diversity

Total species: 1,677
Mean samples per species: 14.9
Gini coefficient: 0.519 (post-balancing)
Acoustic clusters: 3,553 (via K-Means)
Mean cluster salience: 0.378

Quality Metrics

A/B quality rating: 92.1% (Xeno-Canto community ratings)
Manual QA accuracy: 97.8% (n=1,000)
Deduplication rate: 0.03% (13/38,481)

📜 License

Curation Code

MIT License — Free to use, modify, and distribute with attribution.

Audio Clips

Positive samples: Inherit original Xeno-Canto Creative Commons licenses
- CC BY, CC BY-SA, CC BY-NC, CC BY-NC-SA
- Full attribution metadata included in dataset
- See Zenodo for per-recording licenses
Negative samples: Subject to licenses of source datasets:
- BirdVox, Freefield1010, Warblr (DCASE 2018)
- FSC-22, ESC-50, DataSEC
- See source dataset documentation for terms

Important: When using SEABAD, you must:

Credit original Xeno-Canto recordists (metadata provided)
Respect original Creative Commons license terms
Cite SEABAD dataset and paper

📚 Citation

If you use SEABAD or this curation pipeline, please cite:

@dataset{seabad_zenodo2025,
  title   = {{SEABAD} Dataset: 50,000 Southeast Asian Bird Audio Clips},
  year    = {2026},
  url     = {https://zenodo.org/records/18290494},
  note    = {50,000 curated 3-second clips spanning 1,677 Southeast Asian bird species}
}

@misc{zabidi2026seabad,
      title={SEABAD: A Tropical Bird Activity Detection Dataset for Passive Acoustic Monitoring}, 
      author={Muhammad Mun'im Ahmad Zabidi and Mohd Yamani Idna Idris and Norisma Idris},
      year={2026},
      eprint={2605.20853},
      archivePrefix={arXiv},
      primaryClass={cs.SD},
      url={https://arxiv.org/abs/2605.20853}, 
}

Please also credit:

Xeno-Canto and original recordists
Source datasets for negative samples (BirdVox, Freefield1010, Warblr, FSC-22, ESC-50, DataSEC)

🤝 Contributing

We welcome contributions to improve the curation pipeline or extend SEABAD to other regions!

Issues: Report bugs or request features via GitHub Issues
Pull requests: Submit improvements to scripts or documentation
Regional adaptations: Contact us if you're adapting the pipeline for other tropical/temperate regions

🔗 Related Resources

Xeno-Canto: https://www.xeno-canto.org/
AudioMoth: https://www.openacousticdevices.info/
BirdNET: https://birdnet.cornell.edu/
DCASE 2018 Task 3: http://dcase.community/challenge2018/task-bird-audio-detection

📧 Contact

For questions about SEABAD or the curation methodology:

Open an issue: github.com/mun3im/seabad/issues
Email: [contact information]

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Repository files navigation

SEABAD: Southeast Asian Bird Audio Detection Dataset

Overview

🎯 Baseline Validation Results

Key Findings

📂 Repository Structure

🔧 Curation Pipeline

Six-Stage Methodology

Positive Curation Highlights

Negative Curation Highlights

🚀 Quick Start

Prerequisites

1️⃣ Run Positive Pipeline

2️⃣ Run Negative Pipeline

3️⃣ Run Baseline Validation

📦 Pre-Compiled Dataset (Zenodo)

Includes:

🎛️ Audio Processing Standards

📊 Dataset Statistics

Geographic Distribution (Post-Balancing)

Species Diversity

Quality Metrics

📜 License

Curation Code

Audio Clips

📚 Citation

🤝 Contributing

🔗 Related Resources

📧 Contact

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Name		Name	Last commit message	Last commit date
fig		fig
negative-sample-curation		negative-sample-curation
positive-label-curation		positive-label-curation
validation		validation
README.md		README.md

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SEABAD: Southeast Asian Bird Audio Detection Dataset

Overview

🎯 Baseline Validation Results

Key Findings

📂 Repository Structure

🔧 Curation Pipeline

Six-Stage Methodology

Positive Curation Highlights

Negative Curation Highlights

🚀 Quick Start

Prerequisites

1️⃣ Run Positive Pipeline

2️⃣ Run Negative Pipeline

3️⃣ Run Baseline Validation

📦 Pre-Compiled Dataset (Zenodo)

Includes:

🎛️ Audio Processing Standards

📊 Dataset Statistics

Geographic Distribution (Post-Balancing)

Species Diversity

Quality Metrics

📜 License

Curation Code

Audio Clips

📚 Citation

🤝 Contributing

🔗 Related Resources

📧 Contact

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