DOI: 10.5281/zenodo.18156254
title: Zebra Finch Vocalization Dataset for Semi-Automated Clustering

description: Zebra Finch Vocalization Dataset for Semi-Automated Clustering

This dataset supports the paper "Benchmarking Automated and Semi-Automated Vocal Clustering Methods". It contains vocalization data for three juvenile male zebra finches (R3406, R3640, R3822), processed and organized for clustering analysis.

================================================================================Dataset Structure================================================================================

The dataset is organized by subject (bird ID). Each subject folder contains:

1. HDF5 Files (*.h5): Self-contained files storing segmentation, audio features, embeddings, and clustering results.2. Audio Files (wavs/): Original Waveform Audio File Format (.wav) recordings.

--------------------------------------------------------------------------------Directory Layout--------------------------------------------------------------------------------

data/├── R3406/│   ├── wavs/                    Original .wav files│   ├── R3406_unclustered.h5     Base data with segmentation & embeddings (no clusters)│   ├── R3406_hdbscan.h5         Automated clustering results (HDBSCAN)│   ├── R3406_kmeans.h5          Automated clustering results (K-means)│   ├── R3406_manual_expr_*.h5   Fully manual clustering by experts (az, kl, mb)│   └── R3406_semiauto_expr_*.h5 Semi-automated clustering results by experts├── R3640/│   └── ... (same structure)└── R3822/    └── ... (same structure)

================================================================================HDF5 File Contents================================================================================

The HDF5 files follow a standardized structure compatible with the provided Python analysis tools.

1. segments/ Group------------------Contains data for each segmented vocalization (rows correspond to segments):- Timing: onset_sec, duration_sec, onset_sample, duration_samples- Identification: file_id (index into files group), segment_id- Features:   - umap: (N, 2) matrix of UMAP projections of embeddings  - pca: (N, 100) matrix of PCA-reduced embeddings- Clustering: cluster_id (integer label for each segment)

2. files/ Group---------------List of source audio files:- filename: Name of the .wav file- file_id: Unique identifier referenced by segments

3. spectrograms/ Group----------------------Compressed linear spectrograms for each original audio file to facilitate quick visualization.- Keys are file IDs (e.g., '0', '1')

4. embeddings/ Group--------------------Deep learning embeddings for each segment:- raw: High-dimensional embeddings (e.g., from WhisperSeg/Faster-Whisper)- segment_id: Corresponding segment IDs

5. parameters/ Group (Attributes)---------------------------------Metadata and parameters used for preprocessing, organized by step:- Audio: audio_sr (sampling rate), sr_original- Spectrogram: spec_n_fft, spec_hop_length, spec_min_freq, spec_max_freq- Segmentation: seg_model (e.g., WhisperSeg), seg_min_freq, seg_time_step- Embedding: emb_model, emb_batch_size- Dimensionality Reduction: umap_n_neighbors, pca_n_components

================================================================================Usage & Code================================================================================

This dataset is designed for use with the "Semi-Automated Clustering Tool", a Python-based GUI developed for efficient refinement of vocal clusters.- Code Repository: https://github.com/hwiora/semi_automated_clustering/- Documentation: Refer to the repository `README.md` for instructions on loading this data and running the visualization tool.
description: Due to an unknown error, we lost access to and could not update our first publication of the "Zebra Finch Vocalization Dataset for Semi-Automated Clustering" dataset: https://zenodo.org/records/18100613. This current publication is supposed to serve as the second version of the data.