arXiv:2509.24901v4 Announce Type: replace-cross Abstract: Although probing frozen models has become a standard evaluation paradigm, self-supervised learning in audio defaults to fine-tuning when pursuing state-of-the-art on AudioSet. A key reason is that global pooling creates an information bottleneck causing linear probes to misrepresent the embedding quality: The $\texttt{cls}$-token discards crucial token information about dispersed, localized events in audio. This weakness is rooted in the mismatch between the pretraining objective (globally) and the downstream task (localized). Across a
The paper identifies a crucial limitation in current self-supervised learning models for audio, specifically regarding how localized event information is handled, which is critical for real-world applications.
This research addresses a fundamental challenge in applying advanced AI models to audio, potentially unlocking more accurate and efficient audio classification systems crucial for many AI-driven tasks.
The focus shifts towards rethinking architectural components like 'patch tokens' and global pooling in audio AI models to improve their ability to process dispersed, localized events, moving beyond current 'fine-tuning' defaults.
- · AI researchers (audio)
- · Audio classification software developers
- · Autonomous systems (requiring audio awareness)
- · AI model developers
- · Developers relying solely on current global pooling methods for audio
- · Systems with high false negative rates in localized audio event detection
Improved performance in multi-label audio classification tasks with better handling of localized sound events.
Faster adoption of more robust self-supervised learning models in audio applications, reducing the reliance on extensive fine-tuning.
New AI applications leveraging detailed audio understanding emerge, impacting sectors like surveillance, environmental monitoring, and human-computer interaction.
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Read at arXiv cs.LG