Paper Number
ICIS2025-1555
Paper Type
Short
Abstract
Spatial sound scene segmentation is essential for intelligent systems like immersive media, AR, and smart surveillance. This paper introduces Q-SSegNet, a hybrid quantum-classical machine learning model developed within the Design Science Research framework. By utilizing quantum computing, Q-SSegNet is designed to improve feature representation and decrease model complexity. Its effectiveness will be evaluated using both public and proprietary datasets that test its performance on key spatial audio tasks. Additionally, this paper expands the range of information systems (IS) artifacts to include quantum-enabled systems and shows how emerging computational paradigms can be integrated into IS solutions.
Recommended Citation
Ding, Wenwen and Dong, Xindi, "Design Science Approach to Hybrid Quantum-Classical Sound Scene Segmentation" (2025). ICIS 2025 Proceedings. 2.
https://aisel.aisnet.org/icis2025/quantum/quantum/2
Design Science Approach to Hybrid Quantum-Classical Sound Scene Segmentation
Spatial sound scene segmentation is essential for intelligent systems like immersive media, AR, and smart surveillance. This paper introduces Q-SSegNet, a hybrid quantum-classical machine learning model developed within the Design Science Research framework. By utilizing quantum computing, Q-SSegNet is designed to improve feature representation and decrease model complexity. Its effectiveness will be evaluated using both public and proprietary datasets that test its performance on key spatial audio tasks. Additionally, this paper expands the range of information systems (IS) artifacts to include quantum-enabled systems and shows how emerging computational paradigms can be integrated into IS solutions.
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11-Quantum