SpectCount: Spectrotemporal Counting via Synthetic Signals Improves Large Audio Language Models
arXiv:2606.06907v1 Announce Type: cross Abstract: Large audio language models (LALMs) extend large language models with an audio encoder and large-scale audio data. However, the scarcity of high-quality annotated audio data remains a fundamental bottleneck for scaling. Through probing signal detectability analysis, we identify fine-grained spectrotemporal perceptual weaknesses in a foundation LALM. To address these challenges, we propose Spectrotemporal Counting (SpectCount), a data-efficient fine-tuning approach based on fully synthetic audio signals generated on-the-fly, without relying on r
The rapid development of Large Language Models is pushing the boundaries into multimodal AI, making the integration and efficient training of audio data a critical current challenge.
Improving data efficiency for training large audio language models can accelerate their development and deployment, making advanced multimodal AI more accessible and performant.
The ability to use synthetic signals for fine-tuning LALMs reduces reliance on scarce high-quality annotated audio data, potentially lowering compute and data acquisition costs.
- · AI developers
- · Multimodal AI research
- · Audio software companies
- · Companies reliant on large annotated audio datasets for competitive advantage
- · Traditional audio data annotation services
LALMs will become more capable across a wider range of audio tasks, requiring less real-world auditory data for development.
This methodology could be adapted to other data-scarce modalities, accelerating multimodal AI development across the board.
More robust and efficient audio understanding could enable new applications in areas like monitoring, security, and human-computer interaction, impacting various sectors.
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Read at arXiv cs.AI