# Computational bioacoustics with deep learning: a review and roadmap

arXiv: https://arxiv.org/abs/2112.06725 · HTML: https://mlsanity.com/paper/2112.06725/

## Key facts

- Cited by 78 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2021.

## Built on by (most cited first, 30 of 78)

- [Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey](https://mlsanity.com/paper/2302.02515/) (2023)
- [BirdSet: A Large-Scale Dataset for Audio Classification in Avian Bioacoustics](https://mlsanity.com/paper/2403.10380/) (2024)
- [Can Masked Autoencoders Also Listen to Birds?](https://mlsanity.com/paper/2504.12880/) (2025)
- [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)
- [animal2vec and MeerKAT: A self-supervised transformer for rare-event raw audio input and a large-scale reference dataset for bioacoustics](https://mlsanity.com/paper/2406.01253/) (2024)
- [Few-shot bioacoustic event detection at the DCASE 2022 challenge](https://mlsanity.com/paper/2207.07911/) (2022)
- [Self-Supervised Learning for Few-Shot Bird Sound Classification](https://mlsanity.com/paper/2312.15824/) (2023)
- [Foundation Models for Bioacoustics -- a Comparative Review](https://mlsanity.com/paper/2508.01277/) (2025)
- [Unsupervised classification to improve the quality of a bird song recording dataset](https://mlsanity.com/paper/2302.07560/) (2023)
- [AnuraSet: A dataset for benchmarking Neotropical anuran calls identification in passive acoustic monitoring](https://mlsanity.com/paper/2307.06860/) (2023)
- [AudioProtoPNet: An interpretable deep learning model for bird sound classification](https://mlsanity.com/paper/2404.10420/) (2024)
- [Towards Deep Active Learning in Avian Bioacoustics](https://mlsanity.com/paper/2406.18621/) (2024)
- [Can Self-Supervised Neural Representations Pre-Trained on Human Speech distinguish Animal Callers?](https://mlsanity.com/paper/2305.14035/) (2023)
- [Comparing Self-Supervised Learning Models Pre-Trained on Human Speech and Animal Vocalizations for Bioacoustics Processing](https://mlsanity.com/paper/2501.05987/) (2025)
- [Regularized Contrastive Pre-training for Few-shot Bioacoustic Sound Detection](https://mlsanity.com/paper/2309.08971/) (2023)
- [Leveraging tropical reef, bird and unrelated sounds for superior transfer learning in marine bioacoustics](https://mlsanity.com/paper/2404.16436/) (2024)
- [Clustering and novel class recognition: evaluating bioacoustic deep learning feature extractors](https://mlsanity.com/paper/2504.06710/) (2025)
- [Exploring bat song syllable representations in self-supervised audio encoders](https://mlsanity.com/paper/2409.12634/) (2024)
- [Generalization in birdsong classification: impact of transfer learning methods and dataset characteristics](https://mlsanity.com/paper/2409.15383/) (2024)
- [AVEX: What Matters for Animal Vocalization Encoding](https://mlsanity.com/paper/2508.11845/) (2025)
- [Fitting Auditory Filterbanks with Multiresolution Neural Networks](https://mlsanity.com/paper/2307.13821/) (2023)
- [Exploring Meta Information for Audio-based Zero-shot Bird Classification](https://mlsanity.com/paper/2309.08398/) (2023)
- [ISPA: Inter-Species Phonetic Alphabet for Transcribing Animal Sounds](https://mlsanity.com/paper/2402.03269/) (2024)
- [Animal Behavior Analysis Methods Using Deep Learning: A Survey](https://mlsanity.com/paper/2405.14002/) (2024)
- [On the Utility of Speech and Audio Foundation Models for Marmoset Call Analysis](https://mlsanity.com/paper/2407.16417/) (2024)
- [Robust detection of overlapping bioacoustic sound events](https://mlsanity.com/paper/2503.02389/) (2025)
- [Active Bird2Vec: Towards End-to-End Bird Sound Monitoring with Transformers](https://mlsanity.com/paper/2308.07121/) (2023)
- [Future of Edge AI in biodiversity monitoring](https://mlsanity.com/paper/2602.13496/) (2026)

## Abstract (arXiv)

Animal vocalisations and natural soundscapes are fascinating objects of study, and contain valuable evidence about animal behaviours, populations and ecosystems. They are studied in bioacoustics and ecoacoustics, with signal processing and analysis an important component. Computational bioacoustics has accelerated in recent decades due to the growth of affordable digital sound recording devices, and to huge progress in informatics such as big data, signal processing and machine learning. Methods are inherited from the wider field of deep learning, including speech and image processing. However, the tasks, demands and data characteristics are often different from those addressed in speech or music analysis. There remain unsolved problems, and tasks for which evidence is surely present in many acoustic signals, but not yet realised. In this paper I perform a review of the state of the art in deep learning for computational bioacoustics, aiming to clarify key concepts and identify and analyse knowledge gaps. Based on this, I offer a subjective but principled roadmap for computational bioacoustics with deep learning: topics that the community should aim to address, in order to make the most of future developments in AI and informatics, and to use audio data in answering zoological and ecological questions.