About This Project

Lyrebirds are some of the world’s best vocal mimics and can accurately copy dozens of species in their Australian rainforest homes. In this project, we will use sound recordings from the forest to understand just how accurate lyrebird mimicry is. Our goal is to develop machine learning methods for distinguishing lyrebirds from the species they imitate. These methods are foundational for understanding how expert vocal mimics interact with other species in their shared acoustic space in the wild.

Ask the Scientists

What is the context of this research?

Lyrebirds typically imitate over 30 species in their environment. Some species they imitate perfectly: in playback studies, the grey shrike-thrush does not distinguish between their own species calls and lyrebirds' imitations of their calls. But some species they imitate imperfectly, showing clear biases: lyrebirds can not hit the full bandwidth of the eastern whipbird and do not hold the sustained monotone notes as long. Lyrebirds also creatively improvise upon other species' songs, injecting novel variation into the forest soundscape. Scientists are only just beginning to understand what lyrebirds use their incredible vocal abilities for.

What is the significance of this project?

If we want to understand what lyrebird vocal mimicry is used for, we first need strong quantitative tools for comparing the vocalizations of animals in a shared acoustic space. Lyrebird mimicry is an ideal study system for developing these tools because they imitate a wide variety of species with various levels of accuracy. Some applications for these tools will be conservation: passive acoustic monitoring is frequently used to conduct remote censuses, but has difficulty correctly classifying species that use vocal mimicry. Other applications would be for understanding interspecies communication: these tools will help distinguish between two species within the same acoustic space and quantify fine-grained similarities and differences in their acoustic signals.

What are the goals of the project?

Our main goal for this project is to design and collect a large data set of lyrebird vocalizations and the species they imitate in their natural environment. A key component of this project is that we will work with collaborators in machine learning from the start to ensure that the type of data we collect is tailored to the computational methods we plan to use. This data set will be used to 1) map out the shared acoustic space of lyrebirds and the species they imitate, 2) place these species within this space to understand where their signals overlap and where they differ (indicating vocal similarity and distinctiveness) and 3) understand how the variation in these signals is structured over different scales (e.g. single vocalizations as well as longer sequences).