State Space Models for Bioacoustics: A Comparative Evaluation with Transformers
On the map: State-space sequence models (S4, Mamba) → Speech and audio SSMs
Abstract (arXiv)
In this study, we evaluate the efficacy of the Mamba architecture bioacoustics by introducing BioMamba, a Mamba-based audio representation model for wildlife sounds. We pre-train a BioMamba using self-supervised learning on a large audio corpus and evaluate it on the BEANS benchmark across diverse classification and detection tasks. Compared to the state-of-the-art Transformer-based model (AVES), BioMamba achieves comparable performance while significantly reducing VRAM consumption. Our results demonstrate Mamba's potential as a computationally efficient alternative for real-world environmental monitoring.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Speech and audio SSMs, one of 10 directions in State-space sequence models (S4, Mamba). Builds on 22 of its 33 references that are in the map. First posted on arXiv 3 Dec 2025.
Builds on
22 of 33 references.
Closest ancestors · 4
Same direction, newest first
- 2024SELD-Mamba: Selective State-Space Model for Sound Event Localization and Detection with Source Distance Estimation9 Aug
- 2024Speech Slytherin: Examining the Performance and Efficiency of Mamba for Speech Separation, Recognition, and Synthesis13 Jul
- 2024Mamba in Speech: Towards an Alternative to Self-Attention21 May
- 2024SSAMBA: Self-Supervised Audio Representation Learning with Mamba State Space Model20 May
Other references · 18
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
- 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,616
- 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
- 2022FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awarenesscited by 3,611
- 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,301
- 2020Reformer: The Efficient Transformercited by 1,658
- 2020Linformer: Self-Attention with Linear Complexitycited by 1,462
- 2024Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Dualitycited by 1,283
- 2016CNN Architectures for Large-Scale Audio Classificationcited by 701
- 2020VGGSound: A Large-scale Audio-Visual Datasetcited by 641
6 more
- 2024Jamba: A Hybrid Transformer-Mamba Language Modelcited by 390
- 2020FSD50K: An Open Dataset of Human-Labeled Sound Eventscited by 339
- 2021SSAST: Self-Supervised Audio Spectrogram Transformercited by 166
- 2022AVES: Animal Vocalization Encoder based on Self-Supervisioncited by 53
- 2022BEANS: The Benchmark of Animal Soundscited by 37
- 2021HumBugDB: A Large-scale Acoustic Mosquito Datasetcited by 15
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- 2025Synthetic data enables context-aware bioacoustic sound event detection
- 2024Audio Mamba: Selective State Spaces for Self-Supervised Audio Representations
- 2026MambAdapter: Lightweight Mamba-Based Adapters for Parameter-Efficient Transfer Learning in Speech and Audio
- 2024Self-supervised Learning for Acoustic Few-Shot Classification
- 2024Audio Mamba: Bidirectional State Space Model for Audio Representation Learning
- 2024Exploring the Capability of Mamba in Speech Applications
- 2025Advancing Marine Bioacoustics with Deep Generative Models: A Hybrid Augmentation Strategy for Southern Resident Killer Whale Detection
- 2024Generative AI-based data augmentation for improved bioacoustic classification in noisy environments