Abstract
Accurate prediction of bird species from audio recordings is beneficial to bird conservation. Thanks to the rapid advance in deep learning, the accuracy of bird species identification from audio recordings has greatly improved in recent years. This year, the BirdCLEF2019[4] task invited participants to design a system that could recognize 659 bird species from 50,000 audio recordings. The challenges in this competition included memory management, the number of bird species for the machine to recognize, and the mismatch in signal-to-noise ratio between the training and the testing sets. To participate in this competition, we adopted two recently popular convolutional neural network architectures - the ResNet[1] and the inception model[13]. The inception model achieved 0.16 classification mean average precision (c-mAP) and ranked the second place among five teams that successfully submitted their predictions.