ECOGEN : Bird sounds generation using deep learning

• Published in: Methods in ecology and evolution 2024-01, Vol.15 (1), p.69-79
• Main Authors: Guei, Axel‐Christian, Christin, Sylvain, Lecomte, Nicolas, Hervet, Éric
• Format: Article
• Language: English
• Subjects: Acoustics; Audio data; Audio signals; Biologists; bird songs; Birds; Classifiers; computer vision; Data augmentation; Datasets; Deep learning; Digital imaging; Ecological research; Endangered animals; Endangered species; Fourier transforms; Habitat preferences; Image enhancement; Image processing; Insects; Jukeboxes; Machine learning; Musical recordings; Signal classification; Signal generation; Song; Sound generation; sound processing; Spectrograms; variational autoencoders; Vocalization behavior; Wildlife conservation
• ISSN: 2041-210X; 2041-210X
• DOI: 10.1111/2041-210X.14239

Large‐scale acoustic projects generate vast amounts of data that can now be efficiently processed using deep learning tools. However, these tools often face limitations due to sound labeling and imbalanced sampling. Data augmentation can help overcome such challenges, particularly through the generation of synthetic and lifelike sounds. Synthetic samples can be valuable not only for deep learning but also for species with limited available data. Despite advancements in computer power, sound generation remains a time‐consuming process, even requiring a substantial number of samples. We present ECOGEN, a novel deep learning approach designed to generate realistic bird songs for biologists and ecologists. The primary objective of ECOGEN is to enhance the number of samples in under‐represented bird song classes, thereby improving the performance and robustness of classifiers in ecological research.The ECOGEN framework employs spectrograms as a representation of bird songs and leverages proven image generation techniques to create new spectrograms, subsequently converted back to digital audio signals. As a class‐agnostic tool, ECOGEN is applicable to a wide range of biophonic sounds, including mammal and insect calls. We show that adding samples generated by ECOGEN to a bird song classifier improved the classification accuracy by 12% on average and improved results compared with classic data augmentation techniques 80% of the time. Our approach is both fast and efficient, enabling the generation of synthetic bird songs on standard computing resources. By facilitating the creation of synthetic bird songs, ECOGEN can contribute to the conservation of endangered bird species, while providing valuable insights into their vocalizations, behaviours and habitat preferences. Future development of ECOGEN can be easily implemented and could focus on incorporating additional configurable parameters during the generation phase for increased control over the output, catering to the specific needs of biologists.