A1 Journal article (refereed)
Domain‐specific neural networks improve automated bird sound recognition already with small amount of local data (2022)
Lauha, P., Somervuo, P., Lehikoinen, P., Geres, L., Richter, T., Seibold, S., & Ovaskainen, O. (2022). Domain‐specific neural networks improve automated bird sound recognition already with small amount of local data. Methods in Ecology and Evolution, 13(12), 2799-2810. https://doi.org/10.1111/2041-210x.14003
JYU authors or editors
Publication details
All authors or editors: Lauha, Patrik; Somervuo, Panu; Lehikoinen, Petteri; Geres, Lisa; Richter, Tobias; Seibold, Sebastian; Ovaskainen, Otso
Journal or series: Methods in Ecology and Evolution
ISSN: 2041-210X
eISSN: 2041-210X
Publication year: 2022
Publication date: 17/10/2022
Volume: 13
Issue number: 12
Pages range: 2799-2810
Publisher: Wiley-Blackwell
Publication country: United Kingdom
Publication language: English
DOI: https://doi.org/10.1111/2041-210x.14003
Publication open access: Openly available
Publication channel open access: Partially open access channel
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/83704
Abstract
In this article, we demonstrate the workflow for building a global identification model and adjusting it to perform well on the data of autonomous recorders from a specific region. We show how data augmentation and a combination of global and local data can be used to train a convolutional neural network to classify vocalizations of 101 bird species. We construct a model and train it with a global data set to obtain a base model. The base model is then fine-tuned with local data from Southern Finland in order to adapt it to the sound environment of a specific location and tested with two data sets: one originating from the same Southern Finnish region and another originating from a different region in German Alps.
Our results suggest that fine-tuning with local data significantly improves the network performance. Classification accuracy was improved for test recordings from the same area as the local training data (Southern Finland) but not for recordings from a different region (German Alps). Data augmentation enables training with a limited number of training data and even with few local data samples significant improvement over the base model can be achieved. Our model outperforms the current state-of-the-art tool for automatic bird sound classification.
Using local data to adjust the recognition model for the target domain leads to improvement over general non-tailored solutions. The process introduced in this article can be applied to build a fine-tuned bird sound classification model for a specific environment.
Keywords: birds; perceptions (mental objects); animal sounds; recognition; machine learning; deep learning; neural networks (information technology)
Free keywords: autonomous recording units; bioacoustics; bio-monitoring; bird sound recognition; convolutional neural networks; deep learning; model fine-tuning
Contributing organizations
Related projects
- A Planetary Inventory of Life – a New Synthesis Built on Big Data Combined with Novel Statistical Methods
- Ovaskainen, Otso
- European Commission
Ministry reporting: Yes
Reporting Year: 2022
JUFO rating: 2