The Swin Transformer architecture, while effective in capturing local and global dependencies in images, may face challenges in capturing long-range temporal dependencies in audio signals due to the sequential nature of audio data. Audio signals often contain temporal patterns that span across longer timeframes, requiring the model to effectively capture and process these dependencies for accurate reconstruction.
One potential limitation of the Swin Transformer architecture in handling long-range temporal dependencies is the fixed-size window mechanism, which may struggle to capture the entirety of long temporal sequences efficiently. To address this limitation, the architecture could be enhanced by incorporating mechanisms such as hierarchical attention or self-attention mechanisms with adaptive window sizes. These modifications would allow the model to focus on relevant temporal contexts at different scales, enabling better capture of long-range dependencies in audio signals.
Additionally, techniques like dilated convolutions or recurrent neural networks (RNNs) could be integrated into the Swin Transformer architecture to provide additional context and memory for processing long-range temporal dependencies in audio signals. By combining the strengths of different architectures, the Swin Transformer can be optimized to effectively handle the complexities of audio data and improve its performance in capturing long-range temporal dependencies.