AudioSep-hive is a data-efficient, query-based universal sound separation model trained on the Hive dataset. By leveraging the high-quality, semantically consistent Hive dataset, this model achieves competitive separation accuracy and perceptual quality comparable to state-of-the-art models (such as SAM-Audio) while utilizing only a fraction (~0.2%) of the training data volume.

This model is developed by Shanda AI Research Tokyo and is introduced in the paper: A Semantically Consistent Dataset for Data-Efficient Query-Based Universal Sound Separation.

The model is intended for universal sound separation tasks, allowing users to extract specific sounds from complex audio mixtures using multimodal prompts (e.g., text descriptions or audio queries).