A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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-tekijät tai -toimittajat


Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Lauha, Patrik; Somervuo, Panu; Lehikoinen, Petteri; Geres, Lisa; Richter, Tobias; Seibold, Sebastian; Ovaskainen, Otso

Lehti tai sarja: Methods in Ecology and Evolution

ISSN: 2041-210X

eISSN: 2041-210X

Julkaisuvuosi: 2022

Ilmestymispäivä: 17.10.2022

Volyymi: 13

Lehden numero: 12

Artikkelin sivunumerot: 2799-2810

Kustantaja: Wiley-Blackwell

Julkaisumaa: Britannia

Julkaisun kieli: englanti

DOI: https://doi.org/10.1111/2041-210x.14003

Julkaisun avoin saatavuus: Avoimesti saatavilla

Julkaisukanavan avoin saatavuus: Osittain avoin julkaisukanava

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/83704


Tiivistelmä

An automatic bird sound recognition system is a useful tool for collecting data of different bird species for ecological analysis. Together with autonomous recording units (ARUs), such a system provides a possibility to collect bird observations on a scale that no human observer could ever match. During the last decades, progress has been made in the field of automatic bird sound recognition, but recognizing bird species from untargeted soundscape recordings remains a challenge.
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.


YSO-asiasanat: linnut; havainnot; eläinten äänet; tunnistaminen; koneoppiminen; syväoppiminen; neuroverkot

Vapaat asiasanat: autonomous recording units; bioacoustics; bio-monitoring; bird sound recognition; convolutional neural networks; deep learning; model fine-tuning


Liittyvät organisaatiot


Hankkeet, joissa julkaisu on tehty


OKM-raportointi: Kyllä

Raportointivuosi: 2022

JUFO-taso: 2


Viimeisin päivitys 2024-22-04 klo 20:21