Papers › MAX-AST: COMBINING CONVOLUTION, LOCAL AND GLOBAL SELF-ATTENTIONS FOR AUDIO EVENT CLASSIFICATION

MAX-AST: COMBINING CONVOLUTION, LOCAL AND GLOBAL SELF-ATTENTIONS FOR AUDIO EVENT CLASSIFICATION

14 Apr 2024ICASSP 2024 4archive 2025-07-28

Tony Alex, Sara Ahmed, Armin Mustafa, Muhammad Awais, Philip JB Jackson

In the domain of audio transformer architectures, prior research has extensively investigated isotropic architectures that capture the global context through full self-attention and hierarchical architectures that progressively transition from local to global context utilising hierarchical structures with convolutions or window-based attention. However, the idea of imbuing each individual block with both local and global contexts, thereby creating a hybrid transformer block, remains relatively under-explored in the field. To facilitate this exploration, we introduce Multi Axis Audio Spectrogram Transformer (Max-AST), an adaptation of MaxViT to the audio domain. Our approach leverages convolution, local window-attention, and global grid-attention in all the transformer blocks. The proposed model excels in efficiency compared to prior methods and consistently outperforms state-of-the-art techniques, achieving significant gains of up to 2.6% on the AudioSet full set. Further, we performed detailed ablations to analyse the impact of each of these components on audio feature learning. The source code is available at https://github.com/ta012/MaxAST.git

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ta012/MaxAST mentioned in paperpytorch report

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Tasks

Audio Classification

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Audio Classification	AudioSet	MAX-AST (Single)	Test mAP	0.481	#25 of 51	Archive leaderboard	report

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Methods

Absolute Position Encodings Adam Attention BPE Dense Connections Dropout Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Softmax Transformer

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