论文标题
鸟类也需要关注：分析在音景中识别鸟类呼叫时自我注意的用法
The Birds Need Attention Too: Analysing usage of Self Attention in identifying bird calls in soundscapes
论文作者
论文摘要
鸟类是世界各地生态系统的重要部分，是对地球生活质量的绝佳衡量。许多鸟类濒临灭绝，而其他鸟类已经灭绝。理解和监测鸟类种群的生态努力对于保护其栖息地和物种至关重要，但这主要依赖于崎terrain地的手动方法。机器学习和深度学习的最新进展使在各种环境中自动识别自动识别。迄今为止，BirdCall的认可是使用卷积神经网络进行的。在这项工作中，我们尝试了解自我注意力如何帮助这项努力。因此，我们为Birdclef 2022构建了预先训练的基于注意的频谱变压器基线，并将结果与基于预训练的基线基线进行了比较。我们的结果表明，变压器模型的表现优于卷积模型，我们通过构建基准并分析上一年Birdclef 2021挑战的结果进一步验证结果。源代码可从https://github.com/ck090/birdclef-22获得
Birds are vital parts of ecosystems across the world and are an excellent measure of the quality of life on earth. Many bird species are endangered while others are already extinct. Ecological efforts in understanding and monitoring bird populations are important to conserve their habitat and species, but this mostly relies on manual methods in rough terrains. Recent advances in Machine Learning and Deep Learning have made automatic bird recognition in diverse environments possible. Birdcall recognition till now has been performed using convolutional neural networks. In this work, we try and understand how self-attention can aid in this endeavor. With that we build an pre-trained Attention-based Spectrogram Transformer baseline for BirdCLEF 2022 and compare the results against the pre-trained Convolution-based baseline. Our results show that the transformer models outperformed the convolutional model and we further validate our results by building baselines and analyzing the results for the previous year BirdCLEF 2021 challenge. Source code available at https://github.com/ck090/BirdCLEF-22
