Semi-Supervised Sound Event Detection with Conditional Mixup and Embedding-Level Contrastive Loss
arXiv:2606.29901v1 Announce Type: cross Abstract: Sound event detection (SED) is a core module for acoustic environmental analysis, yet its performance is often limited by scarce labeled data. Recent systems leverage large pretrained audio foundation models, but effective fine-tuning remains challenging because labeled data are limited while unlabeled data are abundant. A previous work, ATST-SED, addressed this problem with a pseudo-label based semi-supervised fine-tuning framework. In this work, we further improve the framework by adopting an embedding-level self-supervised contrastive loss i
The proliferation of pretrained audio foundation models creates a need for efficient fine-tuning methods despite limited labeled data, making semi-supervised learning increasingly relevant.
Improved sound event detection can enhance acoustic environmental analysis, enabling more sophisticated AI applications across various sectors with real-world acoustic data.
The proposed method could lead to more accurate and robust real-world sound event detection systems by better leveraging unlabeled data, reducing the reliance on extensive manual labeling.
- · AI developers
- · Acoustic monitoring solutions
- · Environmental analysis platforms
- · Traditional supervised learning methods for SED
- · Companies reliant on large labeled datasets for SED
More efficient and accurate sound event detection models become available for various applications.
Improved acoustic intelligence could lead to advancements in smart city infrastructure, surveillance, and predictive maintenance.
Enhanced ability to interpret ambient soundscapes may accelerate the integration of AI into more nuanced human-like perception tasks.
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Read at arXiv cs.AI