构建12种马来西亚常见鸟类声音数据集，助力热带生物声学研究。
MyGardenBird: A Machine-Learning-Ready Bird Sound Dataset for Twelve Common Malaysian Birds
- 从公开录音库筛选并人工标注7200段音频，每物种600段三秒片段。
- 基于梅尔频谱的卷积网络分类准确率达92%至96%，表明物种间差异明显。
- 数据集开源且附完整处理代码，适合生态监测与鸟类识别研究者使用。
热带地区生物声学数据集仍较匮乏，部分原因在于缺乏可复现的公共档案录音整合流程。本文提出 extbf{MyGardenBird}，一个涵盖马来西亚半岛及印马地区12种常见鸟类的声学数据集。录音源自 Xeno-canto，经物种级筛选、人工频谱切分及质量控制处理。主版本包含7,200段经人工验证的音频片段（16 kHz, 16-bit PCM 单声道 WAV），每物种600段三秒片段（总计6.0小时），来自1,381个独立录音。元数据包括地理坐标、发声类别和信噪比（SNR范围：0.83–59.18 dB，均值：15.80 dB）。另提供44.1 kHz版本。为防止数据泄露，划分以原始录音为单位。基于梅尔频谱的卷积神经网络基线分类实验在测试集上达到92%–96%准确率，表明物种间具有强可区分性。局限在于依赖单标注员，但通过 BirdNET 验证标签一致性。数据集已开放获取（https://doi.org/10.5281/zenodo.20306877），采用 CC BY-NC-SA 4.0 许可，配套完整预处理代码以保障可复现性与未来扩展。
原文摘要 · Abstract (English)
Bioacoustic datasets from tropical regions remain limited, in part due to the absence of reproducible workflows for aggregating recordings from public archives. We present \textbf{MyGardenBird}, a curated dataset of bird vocalisations representing twelve common species across Peninsular Malaysia and the Indo-Malayan region. Recordings were sourced from Xeno-canto and processed through species-level filtering, manual spectrogram segmentation, and quality control checks. The primary release comprises 7,200 manually validated audio clips (16 kHz, 16-bit PCM mono WAV), balanced at 600 three-second clips per species (6.0 hours total) derived from 1,381 distinct recordings. Metadata includes geospatial coordinates, vocalisation categories, and signal-to-noise ratio (SNR) values (range: 0.83--59.18 dB; mean: 15.80 dB). A supplementary 44.1 kHz version is also provided. To mitigate data leakage, dataset partitions are defined at the source-recording level. Baseline classification experiments using convolutional neural networks on Mel-spectrograms achieved test accuracies of 92--96\%, indicating strong interspecies separability. Limitations include reliance on single-annotator curation; however, validation with BirdNET confirmed label consistency. MyGardenBird is openly available at https://doi.org/10.5281/zenodo.20306877 under a CC BY-NC-SA 4.0 licence. Complete preprocessing code accompanies the release to support reproducibility and future expansion.
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