This study emphasizes the significance of exploring distance-based source separation (DSS) in outdoor environments. Unlike existing studies that primarily focus on indoor settings, the proposed model is designed to capture the unique characteristics of outdoor audio sources. It incorporates advanced techniques, including a two-stage conformer block, a linear relation-aware self-attention (RSA), and a TensorFlow Lite GPU delegate. While the linear RSA may not capture physical cues as explicitly as the quadratic RSA, the linear RSA enhances the model's context awareness, leading to improved performance on the DSS that requires an understanding of physical cues in outdoor and indoor environments. The experimental results demonstrated that the proposed model overcomes the limitations of existing approaches and considerably enhances energy efficiency and real-time inference speed on mobile devices.

本研究解决了现有研究主要集中于室内环境的问题，探索了室外环境下的基于距离的源分离（DSS）。提出的模型采用两阶段的conformer block和线性关系感知自注意力（RSA），显著提升了在移动设备上的能源效率和实时推断速度，展示了其在捕捉户内外音源独特特征方面的优势。

移动GPU在室外和室内环境中的基于距离的单通道源分离