BioMamba matches Transformer performance on bioacoustics tasks while using significantly less VRAM.
SSAMBA: Self- supervised audio representation learning with mamba state space model
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A survey that organizes audio SSL into five objective paradigms, relates their demands to architectural biases, and interprets downstream applications as tests of generalization.
The paper consolidates existing research on Mamba models, their architecture variants, adaptations to different data modalities, and applications across domains.
citing papers explorer
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State Space Models for Bioacoustics: A Comparative Evaluation with Transformers
BioMamba matches Transformer performance on bioacoustics tasks while using significantly less VRAM.
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From Objectives to Applications: Aligning Architectural Biases in Audio Self-Supervised Learning
A survey that organizes audio SSL into five objective paradigms, relates their demands to architectural biases, and interprets downstream applications as tests of generalization.
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A Survey of Mamba
The paper consolidates existing research on Mamba models, their architecture variants, adaptations to different data modalities, and applications across domains.