# BEANS: The Benchmark of Animal Sounds

arXiv: https://arxiv.org/abs/2210.12300 · HTML: https://mlsanity.com/paper/2210.12300/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2022.

## Built on by (most cited first, 30 of 38)

- [BirdSet: A Large-Scale Dataset for Audio Classification in Avian Bioacoustics](https://mlsanity.com/paper/2403.10380/) (2024)
- [Perch 2.0: The Bittern Lesson for Bioacoustics](https://mlsanity.com/paper/2508.04665/) (2025)
- [Transferable Models for Bioacoustics with Human Language Supervision](https://mlsanity.com/paper/2308.04978/) (2023)
- [BIRB: A Generalization Benchmark for Information Retrieval in Bioacoustics](https://mlsanity.com/paper/2312.07439/) (2023)
- [The iNaturalist Sounds Dataset](https://mlsanity.com/paper/2506.00343/) (2025)
- [Foundation Models for Bioacoustics -- a Comparative Review](https://mlsanity.com/paper/2508.01277/) (2025)
- [AnuraSet: A dataset for benchmarking Neotropical anuran calls identification in passive acoustic monitoring](https://mlsanity.com/paper/2307.06860/) (2023)
- [Comparing Self-Supervised Learning Models Pre-Trained on Human Speech and Animal Vocalizations for Bioacoustics Processing](https://mlsanity.com/paper/2501.05987/) (2025)
- [Perch 2.0 transfers 'whale' to underwater tasks](https://mlsanity.com/paper/2512.03219/) (2025)
- [AVEX: What Matters for Animal Vocalization Encoding](https://mlsanity.com/paper/2508.11845/) (2025)
- [ISPA: Inter-Species Phonetic Alphabet for Transcribing Animal Sounds](https://mlsanity.com/paper/2402.03269/) (2024)
- [TinyChirp: Bird Song Recognition Using TinyML Models on Low-power Wireless Acoustic Sensors](https://mlsanity.com/paper/2407.21453/) (2024)
- [Crossing the Species Divide: Transfer Learning from Speech to Animal Sounds](https://mlsanity.com/paper/2509.04166/) (2025)
- [WhaleNet: a Novel Deep Learning Architecture for Marine Mammals Vocalizations on Watkins Marine Mammal Sound Database](https://mlsanity.com/paper/2402.17775/) (2024)
- [Finding Needles in the Haystack: Transductive Active Labeling in Ecology](https://mlsanity.com/paper/2606.03821/) (2026)
- [Model Merging Improves Zero-Shot Generalization in Bioacoustic Foundation Models](https://mlsanity.com/paper/2511.05171/) (2025)
- [bacpipe: a Python package to make bioacoustic deep learning models accessible](https://mlsanity.com/paper/2604.11560/) (2026)
- [Dolph2Vec: Self-Supervised Representations of Dolphin Vocalizations](https://mlsanity.com/paper/2606.12503/) (2026)
- [MetaPerch: Learning from metadata for bioacoustics foundation models](https://mlsanity.com/paper/2607.14072/) (2026)
- [Biospheric AI](https://mlsanity.com/paper/2401.17805/) (2024)
- [No Free Lunch from Audio Pretraining in Bioacoustics: A Benchmark Study of Embeddings](https://mlsanity.com/paper/2508.10230/) (2025)
- [AnimalCLAP: Taxonomy-Aware Language-Audio Pretraining for Species Recognition and Trait Inference](https://mlsanity.com/paper/2603.22053/) (2026)
- [From Birdsong to Rumbles: Classifying Elephant Calls with Out-of-Species Embeddings](https://mlsanity.com/paper/2605.00225/) (2026)
- [Multi-layer attentive probing improves transfer of audio representations for bioacoustics](https://mlsanity.com/paper/2605.10494/) (2026)
- [An Entropy-Guided Curriculum Learning Strategy for Data-Efficient Acoustic Scene Classification under Domain Shift](https://mlsanity.com/paper/2509.11168/) (2025)
- [State Space Models for Bioacoustics: A Comparative Evaluation with Transformers](https://mlsanity.com/paper/2512.03563/) (2025)
- [BAT: Better Audio Transformer Guided by Convex Gated Probing](https://mlsanity.com/paper/2602.16305/) (2026)
- [BioVITA: Biological Dataset, Model, and Benchmark for Visual-Textual-Acoustic Alignment](https://mlsanity.com/paper/2603.23883/) (2026)
- [BAGEL: Benchmarking Animal Knowledge Expertise in Language Models](https://mlsanity.com/paper/2604.16241/) (2026)
- [Beyond the Baseband: Adaptive Multi-Band Encoding for Full-Spectrum Bioacoustics Classification](https://mlsanity.com/paper/2604.27936/) (2026)

## Abstract (arXiv)

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 urlhttps://github.com/earthspecies/beans in the hope of establishing a new standard dataset for ML-based bioacoustic research.