Feasibility Study of an AI-Based Selective Noise Cancellation System for Public Environments

Public environments such as buses, trains, offices, and crowded indoor spaces expose people to overlapping sound sources that make it difficult to focus, communicate, or understand speech. Traditional active noise cancellation (ANC) systems primarily target predictable low-frequency noise and cannot isolate a single voice or sound the user wants to hear. This paper explores conceptual design for an AI-based selective noise cancellation system that combines speech separation, microphone array beamforming, and real-time spatial filtering. The system identifies a user-selected audio target, enhances it, and suppresses distracting background signals with minimal latency. Current lightweight neural architectures and embedded AI hardware suggest that such a device is technically feasible for both wearable and environmental applications. This study outlines the system's core components, implementation challenges, and practical deployment considerations. Additionally, the proposed framework highlights potential benefits for accessibility, improved speech comprehension in noisy settings, and future integration with augmented reality audio systems and related emerging technologies in smart environments.

کلیدواژه ها:

Selective Noise Cancellation ، Deep Learning ، Beamforming ، Speech Separation ، Spatial Audio Processing