Data from: Domain-specific neural networks improve automated bird sound recognition already with small amount of local data

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.Models and codes are written in Python and published as Jupyter Notebooks. Audio files are in .mp3 and .wav format and can be opened with any standard audio software.Funding provided by: HORIZON EUROPE European Research CouncilCrossref Funder Registry ID: http://dx.doi.org/10.13039/100019180Award Number: 856506Funding provided by: Academy of FinlandCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100002341Award Number: 309581Funding provided by: Jane and Aatos Erkko FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100004012Award Number: Funding provided by: Norges ForskningsrådCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100005416Award Number: 223257This repository contains data and recognition models described in paper Domain-specific neural networks improve automated bird sound recognition already with small amount of local data. (Lauha et al., 2022)