for - green bean harvester

for - tepary bean - researcher - South Africa

for - stats - tepary bean - drought resistant - can grow in 115 Deg F weather

for - big ag - couldn't control tepary bean - needed no fertilizer - couldn't patent it couldn't modify genetically

for - big ag envied the tepary bean

for - big ag - couldn't control tepary bean - so deemed it unsuitable for production

for - stats - ag - tepary bean - 25% protein - resists pest - use 33% of the water other crops use

for - agriculture - tepary bean - DR Howard Scott - 1974

for - agriculture - tepary bean - mechanization issues? - investigate - mechanization issues - tepary beans

for - threat to colonialism - banned the bean

for - health - anti cancer bean

for - health - super food - tepary bean - high protein bean - more than beef - draught resistant crops

for - stats - agriculture - 3 inches annual rain - bean still produced

for - South African supplier - tepary bean

ValidatorContext 负责生成自定的校验器 Validator Factory 负责生成默认的校验器

• 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.

集合、数组不会