Papers › Self-Attention for Audio Super-Resolution

Self-Attention for Audio Super-Resolution

26 Aug 2021arXiv:2108.11637archive 2025-07-28

Nathanaël Carraz Rakotonirina

Convolutions operate only locally, thus failing to model global interactions. Self-attention is, however, able to learn representations that capture long-range dependencies in sequences. We propose a network architecture for audio super-resolution that combines convolution and self-attention. Attention-based Feature-Wise Linear Modulation (AFiLM) uses self-attention mechanism instead of recurrent neural networks to modulate the activations of the convolutional model. Extensive experiments show that our model outperforms existing approaches on standard benchmarks. Moreover, it allows for more parallelization resulting in significantly faster training.

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