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

1. 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. 2. 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. 3. 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.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.Peer reviewe

Bioacoustics as a research tool for avian ecology and conservation

1. Ecological data from effective survey and monitoring methods are vitally important for evidence-based nature conservation. This need is increasingly being met by technological developments that enable new approaches for collecting biodiversity data. Among these, acoustic techniques can potentially improve the detection and census of vocal taxa such as birds, and can inform habitat quality assessments. 2. Although improvements in hardware and software for acoustic data capture and analysis are providing new tools for scientific researchers and conservation managers, the advancing technology needs to be matched by methodological understanding, good practice, and accepted protocols. These norms and standards do not yet exist for effective application by users. 3. The published work presented here sets out novel research on bird bioacoustics and freshwater ecoacoustics, applying this to species and habitats of high conservation concern. The publications aim to show how the acoustic approach may be used to determine occupancy, assess population size, understand behaviour and determine community characteristics. Vocal activity rates in bird species are studied and occupancy models created, to interpret acoustic data captured in the field. Different song types, potentially related to breeding status, are identified for a priority species. The ecoacoustic approach is used to assess freshwater ecosystem quality, based on the overall soundscape. 4. The results of the published works have been used to better target acoustic monitoring studies and improve the quality of existing survey methods. This knowledge transfer has been enabled by the development and publication of acoustic protocols for bird survey and freshwater habitat assessment. Further testing is still required to establish optimal standard practices for survey and monitoring, but bioacoustics and ecoacoustics offer significant new approaches for more effective monitoring of species and habitats of conservation concern

Proceedings of the 10th International Conference on Ecological Informatics: translating ecological data into knowledge and decisions in a rapidly changing world: ICEI 2018

The Conference Proceedings are an impressive display of the current scope of Ecological Informatics. Whilst Data Management, Analysis, Synthesis and Forecasting have been lasting popular themes over the past nine biannual ICEI conferences, ICEI 2018 addresses distinctively novel developments in Data Acquisition enabled by cutting edge in situ and remote sensing technology. The here presented ICEI 2018 abstracts captures well current trends and challenges of Ecological Informatics towards: • regional, continental and global sharing of ecological data, • thorough integration of complementing monitoring technologies including DNA-barcoding, • sophisticated pattern recognition by deep learning, • advanced exploration of valuable information in ‘big data’ by means of machine learning and process modelling, • decision-informing solutions for biodiversity conservation and sustainable ecosystem management in light of global changes

Proceedings of the 10th International Conference on Ecological Informatics: translating ecological data into knowledge and decisions in a rapidly changing world: ICEI 2018

The Conference Proceedings are an impressive display of the current scope of Ecological Informatics. Whilst Data Management, Analysis, Synthesis and Forecasting have been lasting popular themes over the past nine biannual ICEI conferences, ICEI 2018 addresses distinctively novel developments in Data Acquisition enabled by cutting edge in situ and remote sensing technology. The here presented ICEI 2018 abstracts captures well current trends and challenges of Ecological Informatics towards: • regional, continental and global sharing of ecological data, • thorough integration of complementing monitoring technologies including DNA-barcoding, • sophisticated pattern recognition by deep learning, • advanced exploration of valuable information in ‘big data’ by means of machine learning and process modelling, • decision-informing solutions for biodiversity conservation and sustainable ecosystem management in light of global changes

Good practice guidelines for long-term ecoacoustic monitoring in the UK: with a particular focus on terrestrial biodiversity at the human-audible frequency range

Passive acoustic monitoring has great potential as a cost-effective method for long-term biodiversity monitoring. However, to maximise its efficacy, standardisation of survey protocols is necessary to ensure data are comparable and permit reliable inferences. The aim of these guidelines is to outline a basic long-term acoustic monitoring protocol that can be adapted to suit a range of projects according to specific objectives and size

Good practice guidelines for long-term ecoacoustic monitoring in the UK

The popularity of ecoacoustics as an innovative environmental discipline has enjoyed immensegrowth within the last five years, to a point where it is now becoming difficult to keep up withall the new research papers published. What soon becomes apparent, however, is a lack ofconsensus on which recording and analysis protocols to follow; partly a result of the differingrequirements of each research project, but also an historical artefact of the tropical originsof much of this research. As more acoustic long-term monitoring schemes start to becomeestablished throughout the UK and neighbouring countries there arises a need to adopt a morecommon set of protocols, more akin to our temperate conditions, to allow for valid future analysisand comparison. To that end a group of ecoacoustic researchers and practitioners met in June2022 to discuss the formulation of such a set. This work was then taken forward by the authors togenerate the guidelines contained herein.Digital technologies now allow us the ability to record our acoustic environments widely, withrelative ease; and to subject the resulting recordings to an ever-expanding range of analyticalmethods. This opens up the potential to create new approaches to gauging biodiversity andassessing the changing fortunes of species and their habitats. To maximise these benefits itis vitally important that we secure now, and into the future, data which will illustrate baselineassessments and highlight change. These guidelines therefore provide welcome instruction andconformity, particularly for those new to ecoacoustics. Please use them, as appropriate, to helpguide your own contributions to the growing awareness, and use, of sound as an environmentalmetric within the UK and Europe