使用深度音频嵌入解析Birdsong
Parsing Birdsong with Deep Audio Embeddings
摘要：
监测鸟类种群在保护工作和了解生物多样性丧失方面发挥了至关重要的作用。这一过程的自动化得到了传感技术（如被动声学监测）和相应分析工具（如深度学习）的促进。然而，机器学习模型通常难以推广到训练数据中未遇到的示例。在我们的工作中，我们提出了一个半监督的方法来识别特征调用和环境噪声。我们利用几种方法来学习音频样本的潜在表示，包括卷积自动编码器和两个预先训练的网络，并将所得嵌入分组，供领域专家识别聚类标签。我们表明，我们的方法可以提高分类精度，并提供洞察环境声学数据集的潜在结构。
英文摘要：
Monitoring of bird populations has played a vital role in conservation efforts and in understanding biodiversity loss. The automation of this process has been facilitated by both sensing technologies, such as passive acoustic monitoring, and accompanying analytical tools, such as deep learning. However, machine learning models frequently have difficulty generalizing to examples not encountered in the training data. In our work, we present a semi-supervised approach to identify characteristic calls and environmental noise. We utilize several methods to learn a latent representation of audio samples, including a convolutional autoencoder and two pre-trained networks, and group the resulting embeddings for a domain expert to identify cluster labels. We show that our approach can improve classification precision and provide insight into the latent structure of environmental acoustic datasets.
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