• source
  • https://hyp.is/go?url=https%3A%2F%2Fwww.researchgate.net%2Fpublication%2F364689654_BEANS_The_Benchmark_of_Animal_Sounds&group=world

• 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.

Note that they did not have a mild hyperthyroidism group, whereas they did have a mild hypothyroidism group.