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Audio Mamba: Pretrained Audio State Space Model For Audio Tagging

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arxiv 2405.13636 v1 pith:W2TGRIJQ submitted 2024-05-22 cs.SD cs.AIeess.AS

Jiaju Lin , Haoxuan Hu This is my paper · ORCID
classification cs.SDcs.AIeess.AS
keywords audiomodelsmambaresultsspacespectrogramstatetagging
verification ladder T0 review T1 audit T2 compute T3 formal
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Audio tagging is an important task of mapping audio samples to their corresponding categories. Recently endeavours that exploit transformer models in this field have achieved great success. However, the quadratic self-attention cost limits the scaling of audio transformer models and further constrains the development of more universal audio models. In this paper, we attempt to solve this problem by proposing Audio Mamba, a self-attention-free approach that captures long audio spectrogram dependency with state space models. Our experimental results on two audio-tagging datasets demonstrate the parameter efficiency of Audio Mamba, it achieves comparable results to SOTA audio spectrogram transformers with one third parameters.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Recognizing Dementia from Neuropsychological Tests with State Space Models

    cs.LG 2025-07 reject novelty 5.0 of 10

    Demenba applies Mamba/VMamba state space models to full-length neuropsychological interview audio and reports improved dementia classification AUC over an EfficientNet baseline, but the evaluation has significant sele...

  2. Comparison of spectrogram scaling in multi-label Music Genre Recognition

    cs.SD 2025-06 conditional novelty 4.0 of 10

    On a custom 18k-song multi-label dataset, Mel-scaled spectrograms outperform standard spectrograms for music genre classification with transfer-learned ResNets.

  3. Active Speech Enhancement: Active Speech Denoising Decliping and Deveraberation

    eess.AS 2025-05 conditional novelty 3.0 of 10

    A Transformer-Mamba model that adds a learned correction signal to degraded speech beats adapted active-noise-control baselines on denoising, dereverberation, and declipping in simulation.

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