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  1. Last 7 days
      • 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
        • in the hope of establishing a new standard dataset for ML-based bioacoustic research.
  2. Dec 2022
  3. Feb 2020
  4. Jan 2020
    1. targeting one of three TSH ranges (0.34 to 2.50, 2.51 to 5.60, or 5.61 to 12.0 mU/L)

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

  5. Apr 2017