Acoustic dataset for "Dual‐Signal Buzz Pollination Monitoring: How Flight and Floral Vibrations Complement Each Other to Improve Bee Species Identification"

Published: 16 July 2025| Version 1 | DOI: 10.17632/ggdyfh78d7.1
Contributors:
César Augusto Arvelos, Caique Rocha Resende,
, Lucas Costa Brito, Marcus Duarte,

Description

Research hypothesis We hypothesized that the acoustic signatures of bee floral buzzes and flight sounds encode species-specific patterns that can be automatically distinguished using machine-learning methods. In particular, by combining “static” features (event duration, fundamental frequency, and a selected subset of Mel-frequency cepstral coefficients) with “dynamic” or cinematic features (statistical moments, RMS envelope, spectral band-power in predefined bands, and a rolling‐element fault indicator), a Random Forest classifier can reliably separate floral buzz from flight and further discriminate among five bee species. Data description & collection This dataset comprises 200 individual sound events (100 floral floral buzzes, 100 flight buzzes recordings) from five bee species. For each event we provide: Metadata (buzz_data.csv) id: unique integer identifier type: behavior label (“floral” or “flight” buzzes) Acoustic features duration_s: buzz or flight duration in seconds fundamental_frequency (Hz) Fifteen selected MFCC coefficients (e.g. mel_7, mel_20, …, mel_167) All raw audio files (.wav), per-sample time‐amplitude exports (.txt), and acoustic feature tables are provided. Notable findings Using a Random Forest classifier (200 trees) with 1 000 randomized train/test splits (33 % hold-out each), we observe: Floral buzz classification: 82.2 % ± 6.7 % accuracy Flight buzz classification: 90.9 % ± 5.2 % accuracy Combined classification: 95.0 % ± 3.9 % accuracy Feature-importance consistently ranked fundamental frequency, certain MFCC quantiles (e.g. 75th and 97.5th percentiles of MFCC 5 and 11), and specific envelope band-power bands (310–400 Hz, 110–130 Hz, 225–250 Hz) as most discriminative. Interpretation & reuse Researchers can load the CSV tables into Python, R, or other tools to: Reproduce or benchmark acoustic classification models Explore species-specific acoustic patterns via feature analysis Extend the dataset with new recordings or alternative feature-extraction methods Integrate into automated pollinator monitoring pipelines The accompanying GitHub repository (DOI: …) contains all Python scripts for feature extraction and model training. Users should first run the cinematic-feature extraction script on the raw .txt files, then apply the classification script to reproduce our results. Licensing & citation This dataset is released under CC BY 4.0. Please cite the Mendeley Data DOI and our code DOI (Zenodo) when reusing.

Files

Institutions

Categories

Ecology, Entomology, Life Sciences, Bioacoustics

Funders

Licence