Identifying sequences of syllables within birdsongs is key to tackling a wide array of challenges, including bird individual identification and better understanding of animal communication and sensory-motor learning. Recently, machine learning approaches have demonstrated great potential to alleviate the need for experts to label long audio recordings by hand. However, they still typically rely on the availability of labelled data for model training, restricting applicability to a few species and datasets. In this work, we build the first fully unsupervised algorithm to decompose birdsong recordings into sequences of syllables. We first detect syllable events, then cluster them to extract templates -- syllable representations -- before performing matching pursuit to decompose the recording as a sequence of syllables. We evaluate our automatic annotations against human labels on a dataset of Bengalese finch songs and find that our unsupervised method achieves high performance. We also demonstrate that our approach can distinguish individual birds within a species through their unique vocal signatures, for both Bengalese finches and another species, the great tit.

New research demonstrates for the first time that artificial intelligence (AI) can be used to train computers to recognize individual birds, a task humans are unable to do. The research is published in the British Ecological Society journal Methods in Ecology and Evolution. "We show that computers can consistently recognize dozens of individual birds, even though we cannot ourselves tell these individuals apart. In doing so, our study provides the means of overcoming one of the greatest limitations in the study of wild birds--reliably recognizing individuals." Said Dr. André Ferreira at the Center for Functional and Evolutionary Ecology (CEFE), France, and lead author of the study.

An immense frustration ecologists encounter is prompted by the attempt to keep track of individual animals in a study. This task only becomes more difficult when trying to pinpoint small, mobile animals like songbirds. While intelligent computer algorithms can help scientists better complete this task, training these systems to recognize different species -- let alone individuals in a species -- can take thousands of data points, time, and money. However, French and Portuguese researchers recently devised a way to streamline this process. They designed a deep-learning network that can identify individual birds with up to 92 percent accuracy in three different species. This tech can not only save scientists resources but can help them collect important data about the lives of birds -- and better understand what may be leading to their decline in North America.

Distinguishing between individual animals is important for long-term monitoring of populations and protecting species from pressures such as climate change. However, it is also one of the most expensive, troublesome, and time-consuming aspects of animal behaviour research. While some creatures such as leopards have unique markings which allow humans to recognise individuals by eye, most species require additional visual identifiers such as coloured bands to be distinguished. Attaching bands to birds' legs can be stressful and disruptive to the animals, limiting the scope of research. Seeking an alternative method for distinguishing between individual birds, researchers from institutes in France, Germany, Portugal, and South Africa developed the first AI bird identification tool of its kind.

Individual identification is a crucial step to answer many questions in evolutionary biology and is mostly performed by marking animals with tags. Such methods are well‐established, but often make data collection and analyses time‐consuming, or limit the contexts in which data can be collected. Recent computational advances, specifically deep learning, can help overcome the limitations of collecting large‐scale data across contexts. However, one of the bottlenecks preventing the application of deep learning for individual identification is the need to collect and identify hundreds to thousands of individually labelled pictures to train convolutional neural networks (CNNs). Here we describe procedures for automating the collection of training data, generating training datasets, and training CNNs to allow identification of individual birds.

Differentiating between individuals of a same species is essential in the study of wild animals, their processes of adaptation and behaviour. Scientists from the CEFE research centre in Ecology and Evolutionary Ecology (CNRS/ Université de Montpellier/ Université Paul-Valéry-Montpellier/ IRD/ EPHE) and the Research Centre in Biodiversity and Genetic Resources (CIBIO) at Porto University have for the very first time identified individual birds with the help of artificial intelligence technology. They have developed a technique that enables them to gather a large number of photographs, taken from various angles, of individual birds wearing electronic tags. These images were fed into computers which used deep learning technology to recognise the birds by analysing the photographs. The computers were able to distinguish individual birds according to the patterns on their plumage, something humans can't do.

If you saw a finch one time, chances are you'd have great difficulty picking it out from a large group of finches later on. A new artificial intelligence-based system can do just that, though, potentially making life much easier for both biologists and the birds that they study. Ordinarily, if a wildlife biologist wants to track an individual bird, they have to capture it, put an identity band on its leg, release it, then later recapture it to read that band. Needless to say, doing so is quite a hassle for the scientist, and very stressful to the bird. There are now also remotely readable GPS tags, although these still have to initially be attached to the animal.

Trying to identify a wild bird while frantically leafing through a bird-spotters' guide is no easy task. But modern technology has come to the rescue, with artificial intelligence trained to help out amateur twitchers. Where people may be confused by two similar looking birds, or a juvenile which does not yet have its adult plumage, AI has been found to identify birds with up to around 90 per cent accuracy. The technology was trained using pictures of wild great tits and sociable weavers, as well as captive zebra finches. It works in a similar way to the face-recognition programmes used to identify people in crowds.

As a birder, I had heard that if you paid careful attention to the head feathers on the downy woodpeckers that visited your bird feeders, you could begin to recognize individual birds. I even went so far as to try sketching birds at my own feeders and had found this to be true, up to a point. In the meantime, in my day job as a computer scientist, I knew that other researchers had used machine learning techniques to recognize individual faces in digital images with a high degree of accuracy. These projects got me thinking about ways to combine my hobby with my day job. Would it be possible to apply those techniques to identify individual birds?