Animal vocalizations will be synthesised using various artificial intelligence techniques to interrogate how computation enables new sonic experiences of 'nature' in the contexts of conservation and ecologically-inspired musical composition.

Principal investigator

Sergio Renteria Aguilar
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Area of science


Systems used


Applications used

Audio generative models for PyTorch
Partner Institution: The University of Western Australia | Project Code: 48b1b73c3b59439aa8bbac54e93c8e40

The Challenge

To generate high-fidelity synthetic animal vocalizations and soundscapes from wildlife sound archives and create a software to manipulate them according to musical principles.

The Solution

A machine learning technique (variational autoencoder) capable of generating high-fidelity audio from publicly available sound libraries.

The Outcome

By speeding up the training process through NVIDIA V100 GPUs.