Single-Channel Distance-Based Source Separation for Mobile GPU in Outdoor and Indoor Environments
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917884798697472 |
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| author | Bae, Hanbin Kang, Byungjun Kim, Jiwon Hwang, Jaeyong Sung, Hosang Cho, Hoon-Young |
| author_facet | Bae, Hanbin Kang, Byungjun Kim, Jiwon Hwang, Jaeyong Sung, Hosang Cho, Hoon-Young |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_03045 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Single-Channel Distance-Based Source Separation for Mobile GPU in Outdoor and Indoor Environments Bae, Hanbin Kang, Byungjun Kim, Jiwon Hwang, Jaeyong Sung, Hosang Cho, Hoon-Young Audio and Speech Processing Artificial Intelligence 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. |
| title | Single-Channel Distance-Based Source Separation for Mobile GPU in Outdoor and Indoor Environments |
| topic | Audio and Speech Processing Artificial Intelligence |
| url | https://arxiv.org/abs/2501.03045 |