mlsanityData to 14 Oct 2026

Computational bioacoustics with deep learning: a review and roadmap

arXiv 2112.06725 ↗First posted 13 Dec 2021cs.SD eess.AS q-bio.QMcited by 78

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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.

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

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 15; 2024: 12; 2025: 23; 2026: 26.

78 papers.

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  1. 2023Deep Learning for Time Series Classification and Extrinsic Regression: A Current Surveycited by 68
  2. 2024BirdSet: A Large-Scale Dataset for Audio Classification in Avian Bioacousticscited by 36
  3. 2025Can Masked Autoencoders Also Listen to Birds?cited by 26
  4. 2025Perch 2.0: The Bittern Lesson for Bioacousticscited by 26
  5. 2023Transferable Models for Bioacoustics with Human Language Supervisioncited by 21
  6. 2023BIRB: A Generalization Benchmark for Information Retrieval in Bioacousticscited by 19
  7. 2024animal2vec and MeerKAT: A self-supervised transformer for rare-event raw audio input and a large-scale reference dataset for bioacousticscited by 14
  8. 2022Few-shot bioacoustic event detection at the DCASE 2022 challengecited by 13
  9. 2023Self-Supervised Learning for Few-Shot Bird Sound Classificationcited by 11
  10. 2025Foundation Models for Bioacoustics -- a Comparative Reviewcited by 11
  11. 2023Unsupervised classification to improve the quality of a bird song recording datasetcited by 10
  12. 2023AnuraSet: A dataset for benchmarking Neotropical anuran calls identification in passive acoustic monitoringcited by 10
38 more
  1. 2024AudioProtoPNet: An interpretable deep learning model for bird sound classificationcited by 10
  2. 2024Towards Deep Active Learning in Avian Bioacousticscited by 10
  3. 2023Can Self-Supervised Neural Representations Pre-Trained on Human Speech distinguish Animal Callers?cited by 9
  4. 2025Comparing Self-Supervised Learning Models Pre-Trained on Human Speech and Animal Vocalizations for Bioacoustics Processingcited by 9
  5. 2023Regularized Contrastive Pre-training for Few-shot Bioacoustic Sound Detectioncited by 7
  6. 2024Leveraging tropical reef, bird and unrelated sounds for superior transfer learning in marine bioacousticscited by 7
  7. 2025Clustering and novel class recognition: evaluating bioacoustic deep learning feature extractorscited by 7
  8. 2024Exploring bat song syllable representations in self-supervised audio encoderscited by 5
  9. 2024Generalization in birdsong classification: impact of transfer learning methods and dataset characteristicscited by 5
  10. 2025AVEX: What Matters for Animal Vocalization Encodingcited by 5
  11. 2023Fitting Auditory Filterbanks with Multiresolution Neural Networkscited by 4
  12. 2023Exploring Meta Information for Audio-based Zero-shot Bird Classificationcited by 4
  13. 2024ISPA: Inter-Species Phonetic Alphabet for Transcribing Animal Soundscited by 4
  14. 2024Animal Behavior Analysis Methods Using Deep Learning: A Surveycited by 4
  15. 2024On the Utility of Speech and Audio Foundation Models for Marmoset Call Analysiscited by 4
  16. 2025Robust detection of overlapping bioacoustic sound eventscited by 4
  17. 2023Active Bird2Vec: Towards End-to-End Bird Sound Monitoring with Transformerscited by 3
  18. 2026Future of Edge AI in biodiversity monitoringcited by 3
  19. 2023Instabilities in Convnets for Raw Audiocited by 2
  20. 2024Self-supervised Learning for Acoustic Few-Shot Classificationcited by 2
  21. 2025Synthetic data enables context-aware bioacoustic sound event detectioncited by 2
  22. 2025Aggregation Strategies for Efficient Annotation of Bioacoustic Sound Events Using Active Learningcited by 2
  23. 2025Hybrid Disagreement-Diversity Active Learning for Bioacoustic Sound Event Detectioncited by 2
  24. 2025WoW-Bench: Evaluating Fine-Grained Acoustic Perception in Audio-Language Models via Marine Mammal Vocalizationscited by 2
  25. 2025Model Merging Improves Zero-Shot Generalization in Bioacoustic Foundation Modelscited by 2
  26. 2026bacpipe: a Python package to make bioacoustic deep learning models accessiblecited by 2
  27. 2026Determinantal point process sampling for bioacoustic active learningcited by 2
  28. 2026MetaPerch: Learning from metadata for bioacoustics foundation modelscited by 2
  29. 2022Propagating Variational Model Uncertainty for Bioacoustic Call Label Smoothingcited by 1
  30. 2024Generative AI-based data augmentation for improved bioacoustic classification in noisy environmentscited by 1
  31. 2025acoupi: An Open-Source Python Framework for Deploying Bioacoustic AI Models on Edge Devicescited by 1
  32. 2025Localized time-frequency representation learning for bioacoustic classification in complex soundscapescited by 1
  33. 2025Weakly Supervised Detection and Temporal Localization of Whale Calls in Long-Duration Bioacoustic Datacited by 1
  34. 2025A Bird Song Detector for improving bird identification through Deep Learning: a case study from Do~nanacited by 1
  35. 2025First-of-its-kind AI model for bioacoustic detection using a lightweight associative memory Hopfield neural networkcited by 1
  36. 2025No Free Lunch from Audio Pretraining in Bioacoustics: A Benchmark Study of Embeddingscited by 1
  37. 2025Explainability of CNN Based Classification Models for Acoustic Signalcited by 1
  38. 2025Uncertainty Calibration of Multi-Label Bird Sound Classifierscited by 1

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