Zebra Finch Vocalization Dataset for Semi-Automated Clustering

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.