Rethinking Masking Strategies for Masked Prediction-based Audio Self-supervised Learning
This work addresses computational efficiency and generalization issues in audio representation learning for researchers, though it is incremental as it builds on existing masking methods.
The paper tackled the computational overhead and generalization trade-offs of informed masking techniques in audio self-supervised learning by proposing dispersion-weighted masking (DWM), a lightweight strategy that leverages spectral sparsity, resulting in consistent performance improvements.
Since the introduction of Masked Autoencoders, various improvements to masking techniques have been explored. In this paper, we rethink masking strategies for audio representation learning using masked prediction-based self-supervised learning (SSL) on general audio spectrograms. While recent informed masking techniques have attracted attention, we observe that they incur substantial computational overhead. Motivated by this observation, we propose dispersion-weighted masking (DWM), a lightweight masking strategy that leverages the spectral sparsity inherent in the frequency structure of audio content. Our experiments show that inverse block masking, commonly used in recent SSL frameworks, improves audio event understanding performance while introducing a trade-off in generalization. The proposed DWM alleviates these limitations and computational complexity, leading to consistent performance improvements. This work provides practical guidance on masking strategy design for masked prediction-based audio representation learning.