We lifted 4 functions out of this paper's own repositories and ran 4 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| earthspecies/beans | canonical | 4 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| frame | Ran | earthspecies/beans/beans/torchvggish/mel_features.py code served (permissive licence) · get_code("bb4f7d8a2d24296f") |
| get_md5 | Ran | earthspecies/beans/beans/utils.py code served (permissive licence) · get_code("52f965e4585d8dea") |
| periodic_hann | Ran | earthspecies/beans/beans/torchvggish/mel_features.py code served (permissive licence) · get_code("631cab04cf31d247") |
| stft_magnitude | Ran | earthspecies/beans/beans/torchvggish/mel_features.py code served (permissive licence) · get_code("fcb574ecc2b946ef") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
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.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2210.12300")
get_code_for_paper("2210.12300")
have("2210.12300")
Connect an agent — have() is free.