RT info:eu-repo/semantics/article
T1 Multispecies bird sound recognition using a fully convolutional neural network
A1 García Ordás, María Teresa
A1 Rubio Martín, Sergio
A1 Benítez Andrades, José Alberto
A1 Alaiz Moretón, Héctor
A1 García Rodríguez, Isaías
A2 Ingenieria de Sistemas y Automatica
K1 Informática
K1 Ingeniería de sistemas
K1 sound recognition
K1 machine learning
K1 FCNN
K1 CNN
K1 3306 Ingeniería y Tecnología Eléctricas
K1 33 Ciencias Tecnológicas
AB [EN] 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.
PB Springer
SN 0924-669X
LK https://hdl.handle.net/10612/18170
UL https://hdl.handle.net/10612/18170
NO García-Ordás, M. T., Rubio-Martín, S., Benítez-Andrades, J. A., Alaiz-Moretón, H., & García-Rodríguez, I. (2023). Multispecies bird sound recognition using a fully convolutional neural network. Applied Intelligence, 53(20), 23287-23300. https://doi.org/10.1007/S10489-023-04704-3
DS BULERIA. Repositorio Institucional de la Universidad de León
RD 11-jun-2024