Researchers have introduced ALM2Vec, a novel framework designed to create universal audio embeddings by leveraging large audio-language models (LALMs). Unlike previous methods focused on audio-caption matching, ALM2Vec aims to support a wider range of retrieval objectives and controllable behaviors. The framework transfers capabilities from LALMs, enabling instruction-aware retrieval for tasks like audio question answering and aspect-conditioned retrieval. Experiments indicate that ALM2Vec performs competitively on standard benchmarks while demonstrating potential for unified audio embedding across diverse domains and user intents. AI
IMPACT This framework could enable more versatile and controllable audio retrieval systems by leveraging large language models.
RANK_REASON The cluster contains a research paper detailing a new method for audio embeddings. [lever_c_demoted from research: ic=1 ai=1.0]
- ALM2Vec
- alphaXiv
- arXiv
- CatalyzeX
- DagsHub
- Gotit.pub
- Hugging Face
- Large Audio-Language Models
- ScienceCast
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