  This study proposes a method based on fully convolutional neural networks
(FCNs) to identify migratory birds from their songs, with the objective of
recognizing which birds pass through certain areas and at what time. To
determine the best FCN architecture, extensive experimentation was conducted
through a grid search, exploring the optimal depth, width, and activation
function of the network. The results showed that the optimal number of filters
is 400 in the widest layer, with 4 convolutional blocks with maxpooling and an
adaptive activation function. The proposed FCN offers a significant advantage
over other techniques, as it can recognize the sound of a bird in audio of any
length with an accuracy greater than 85%. Furthermore, due to its architecture,
the network can detect more than one species from audio and can carry out
near-real-time sound recognition. Additionally, the proposed method is
lightweight, making it ideal for deployment and use in IoT devices. The study
also presents a comparative analysis of the proposed method against other
techniques, demonstrating an improvement of over 67% in the best-case scenario.
These findings contribute to advancing the field of bird sound recognition and
provide valuable insights into the practical application of FCNs in real-world
scenarios.
