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.

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.