arXiv:2605.22262v1 Announce Type: cross Abstract: Audio context determines which sound components and sources are relevant and which can be perceived as irrelevant (noise) by listeners. For example, traffic noise is informative in urban surveillance but noise for a phone call at the same location. Most current audio denoising systems apply fixed target-noise definitions, often removing useful components in one context while failing to suppress irrelevant components. To address this, we introduce the concept automatic contextual audio denoising (ACAD) which defines target and noise based on the
The proliferation of audio-centric AI applications and the increasing sophistication of machine learning models make contextual audio processing a natural next step.
This development could significantly improve the robustness and utility of AI systems that rely on audio input, making them more adaptable to real-world environments.
Audio processing and denoising will transition from fixed, general definitions of 'noise' to dynamic, context-aware determinations, enhancing AI's perceptual accuracy.
- · AI-powered voice assistants
- · Surveillance technology providers
- · Telecommunications companies
- · Audio software developers
- · Manufacturers of generic noise-cancelling hardware
- · Companies reliant on simple audio filtering
- · Legacy audio processing software
AI audio systems will demonstrate improved performance in complex and noisy environments.
This improved performance could enable new applications in critical sectors like healthcare, defense, and urban monitoring.
Contextual audio understanding might contribute to more advanced AI agents capable of nuanced environmental interpretation and interaction.
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Read at arXiv cs.LG