论文标题
基于机器学习通过鸟类对鸟类的分类
Machine Learning-based Classification of Birds through Birdsong
论文作者
论文摘要
音频声音识别和分类用于许多任务和应用程序，包括人音识别，音乐识别和音频标签。在本文中，我们将MEL频率曲线系数（MFCC）与一系列机器学习模型结合使用，以识别（澳大利亚）鸟类的鸟类鸟类的公开音频文件。我们介绍了用于数据处理和增强的方法，并比较了最先进的机器学习模型的结果。我们从被选为案例研究的30只前5只鸟类中实现了91％的总体精度。将模型应用于包含152种鸟类的更具挑战性和不同的音频文件，我们的精度为58％
Audio sound recognition and classification is used for many tasks and applications including human voice recognition, music recognition and audio tagging. In this paper we apply Mel Frequency Cepstral Coefficients (MFCC) in combination with a range of machine learning models to identify (Australian) birds from publicly available audio files of their birdsong. We present approaches used for data processing and augmentation and compare the results of various state of the art machine learning models. We achieve an overall accuracy of 91% for the top-5 birds from the 30 selected as the case study. Applying the models to more challenging and diverse audio files comprising 152 bird species, we achieve an accuracy of 58%
