for: animal communication, AI - animal communication, bioacoustic

title: BEAN: The Benchmark of Animal Sounds

author

• Masato Hagiwara
• Benjamin Hoffman
• Jen-Yu Liu
• Maddie Cusimano

Abstract

• The use of machine learning (ML) based techniques has become increasingly popular in the field of bioacoustics over the last years.
• Fundamental requirements for the successful application of ML based techniques are curated, agreed upon, high-quality datasets and benchmark tasks to be learned on a given dataset.
• However, the field of bioacoustics so far lacks such public benchmarks which cover multiple tasks and species to measure the performance of ML techniques in a controlled and standardized way and that allows for benchmarking newly proposed techniques to existing ones.
• Here, we propose BEANS (the BEnchmark of ANimal Sounds), a collection of bioacoustics tasks and public datasets, specifically designed to measure the performance of machine learning algorithms in the field of bioacoustics.
• The benchmark proposed here consists of two common tasks in bioacoustics:
  • classification and
  • detection.
• It includes 12 datasets covering various species, including
  • birds,
  • land and marine mammals,
  • anurans, and insects.
• In addition to the datasets, we also present the performance of a set of standard ML methods as the baseline for task performance.
• The benchmark and baseline code is made publicly available at
  • \url{https://github.com/earthspecies/beans}
• in the hope of establishing a new standard dataset for ML-based bioacoustic research.