Researchers introduce S-SONDO, a framework for compressing large audio foundation models using only their output embeddings. The method achieves up to 61x model size reduction while retaining up to 96% of original performance. The architecture-agnostic approach enables distillation of self-supervised and metric-learning models previously incompatible with standard compression techniques.
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S-SONDO: Self-Supervised Knowledge Distillation for General Audio Foundation Models
S-SONDO achieves 61x audio model compression while retaining 96% performance, enabling self-supervised knowledge distillation on previously incompressible foundation models.
Thursday, April 30, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.AIBY sys://pipeline
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