USAD 2.0: Scaling Representation Distillation for Universal Audio Understanding
Audio encoders are critical to modern audio applications as large language models (LLMs) increasingly rely on a single encoder for diverse inputs. While self-supervised learning (SSL) has yielded strong domain-specific encoders like speech or music experts, multi-domain approaches like USAD and SPEAR remain limited in coverage and evaluation. Recent studies also suggest supervised encoders align better with audio LLMs. We present USAD 2.0, a universal encoder integrating knowledge from both SSL and supervised foundation models. USAD 2.0 introduces domain-aware distillation to address teacher mismatch, extends coverage to the music domain, and adds second-stage supervised distillation for ...
| Authors | Heng-Jui Chang, Alexander H. Liu, Saurabhchand Bhati et al. |
|---|---|
| Primary category | eess.AS |
| Published | 4 June 2026 |
| Last updated at source | 4 June 2026 |
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