arXiv:2412.03771v3 Announce Type: replace-cross Abstract: Zero-shot learning enables models to generalise to unseen classes by leveraging semantic information, bridging the gap between training and testing sets with non-overlapping classes. While much research has focused on zero-shot learning in computer vision, the application of these methods to environmental audio remains underexplored, with poor performance in existing studies. Generative methods, which have demonstrated success in computer vision, are notably absent from zero-shot environmental sound classification studies. To address th
The continuous rapid advancements in AI, particularly generative models, are enabling breakthroughs in specialized applications like environmental sound classification that were previously challenging.
Improving zero-shot learning for environmental audio expands AI's ability to interpret complex real-world sensor data without extensive prior training, opening new avenues for monitoring and analysis across various domains.
The application of generative methods, specifically diffusion models, to environmental sound classification suggests a path to more robust and generalized AI understanding of auditory data, reducing reliance on large labeled datasets.
- · AI researchers (esp. audio AI)
- · Environmental monitoring agencies
- · Security and surveillance sectors
- · Smart city developers
- · Companies relying on manual audio data labeling
- · Older, supervised learning audio classification methods
More accurate and efficient classification of diverse environmental sounds will become feasible.
This could lead to widespread deployment of autonomous acoustic sensors for monitoring wildlife, infrastructure, or security events.
Enhanced environmental awareness through ubiquitous audio AI might influence policy decisions related to urban planning, conservation, and noise pollution control.
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Read at arXiv cs.LG