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 📄 Paper: SEABAD: A Tropical Bird Audio Detection Dataset for Passive Acoustic Monitoring (2025) 💻 Code: github.com/mun3im/seabad

---

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

1. Metadata Acquisition — Xeno-Canto API query for Southeast Asian bird species
2. Sound Acquisition — Download + convert to 16 kHz mono FLAC
3. Acoustic Deduplication — FAISS approximate nearest-neighbor on mel embeddings
4. Segment Extraction — RMS-based sliding window with minimum separation
5. Species Balancing — Diversity-aware balancing using MiniBatch K-Means + salience ranking
6. 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:

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

---

📚 Citation

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

@article{seabad2025,
  title   = {{SEABAD}: A Tropical Bird Audio Detection Dataset for Passive Acoustic Monitoring},
  author  = {Author Names},
  journal = {arXiv preprint arXiv:XXXX.XXXXX},
  year    = {2025},
  url     = {https://github.com/mun3im/seabad}
}

@dataset{seabad_zenodo2025,
  title   = {{SEABAD} Dataset: 50,000 Southeast Asian Bird Audio Clips},
  year    = {2025},
  url     = {https://zenodo.org/records/18290494},
  note    = {50,000 curated 3-second clips spanning 1,677 Southeast Asian bird species}
}

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]

---

Built with ❤️ for tropical biodiversity monitoring