CONTEXT-AWARE NOISE SUPPRESSION USING MULTIMODAL AI

Noise suppression and enhancement technologies play a vital role in modern communication systems, especially in video conferencing platforms such as Google Meet, online collaboration tools, and virtual learning environments. Traditional adaptive noise cancellation methods rely mainly on unimodal audio input and low-level acoustic processing, which often proves insufficient in complex real-world environments, leading to the loss of meaningful auditory information.

This paper proposes a context-aware noise suppression framework based on multimodal artificial intelligence to overcome these limitations. The framework integrates audio, visual, and motion-based contextual information to enable semantic-level understanding of sound sources. Audio signals are analyzed using speech and noise classification models, while visual and motion inputs assist in determining spatial orientation and contextual relevance. A unified decision mechanism conceptually determines whether sounds should be preserved or suppressed based on surrounding context.

The proposed approach is expected to improve speech clarity, enhance user focus, and maintain environmental awareness. It is particularly relevant for applications such as video conferencing, wireless headphones, smart earbuds, assistive hearing devices, gaming headsets, and safety-critical communication systems, highlighting the importance of multimodal intelligence in next-generation noise suppression technologies.