ALM2Vec: Learning Audio Embeddings for Universal Audio Retrieval with Large Audio-Language Models
arXiv:2606.30682v1 Announce Type: cross Abstract: Recent advances in language--audio retrieval have been largely driven by contrastive dual-encoder architectures that align audio and text in a shared embedding space. While effective, existing retrieval embeddings are primarily optimized for audio--caption matching, limiting their ability to support diverse retrieval objectives and controllable retrieval behaviors. We present ALM2Vec, a universal audio embedding framework derived from pretrained large audio--language models (LALMs). By transferring the audio understanding, instruction-following
The proliferation of advanced large audio-language models (LALMs) provides the foundational technology needed to create more universal and adaptable audio embeddings.
This development allows for more sophisticated and versatile audio retrieval, moving beyond simple caption matching to support diverse, controllable objectives that are critical for AI applications.
Audio retrieval systems are becoming more powerful and nuanced, capable of understanding and responding to complex instructions rather than just keyword matches.
- · AI developers
- · Content creators
- · Speech recognition companies
- · Audio analysis platforms
- · Legacy audio search engines
- · Developers reliant on simple audio-caption matching
Improved accuracy and flexibility of audio search and organization across various platforms.
New AI-powered applications emerge that leverage highly sophisticated audio understanding for tasks like content generation or security.
The increased power of audio intelligence contributes to the broader integration of AI into more sensory and contextual understanding systems.
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Read at arXiv cs.AI