Identification of Beehive Piping Audio Signals

We introduce a novel dataset of bee piping audio signals which was built by collecting 44 different recordings which were published by various beekeepers on the YouTube platform.Each recording has a duration varying from 2 to 13 seconds and is annotated according to the beekeeper comment respectively as Tooting or Quacking.We extracted the audio using ``YouTube soundtrack extraction'' from 14 distinct videos from which the signal is stored without a loss of quality into a WAVE file with a sampling frequency of F_s=22.05 kHz and a sample precision of 16 bits.After removing the silent frames, the resulting dataset contains 36 tooting and 8 quacking signals which correspond to a duration of 145 seconds for tooting and 60 seconds for quacking (total 205 seconds).For copyright reasons, we only made publicly available the short-time Fourier transforms matrices and the timbre descriptors computed using a matlab implementation of the timbre toolbox proposed by Peeters et al. in 2011.A more detailed description of the dataset containing the links of the original youtube videos can be found

本研究提出了一套全新的蜜蜂蜂鸣音频信号数据集，该数据集通过收集44条由不同养蜂人发布于YouTube平台的录音构建而成。每条录音的时长介于2至13秒之间，并依据发布养蜂人的评论分别标注为吹鸣（Tooting）与嘎嘎鸣（Quacking）。本研究通过「YouTube音频提取工具」从14个独立视频中提取音频，将无质量损耗的音频信号存储为WAVE格式文件，其采样频率为F_s=22.05 kHz，采样精度为16比特。移除静音帧后，最终数据集共包含36条吹鸣信号与8条嘎嘎鸣信号，其中吹鸣信号总时长为145秒，嘎嘎鸣信号总时长为60秒，合计总时长205秒。出于版权考量，本研究仅公开了短时傅里叶变换矩阵，以及采用Peeters等人2011年提出的音色工具箱的MATLAB实现所计算得到的音色描述符。包含原始YouTube视频链接的数据集详细说明可查阅