A Human-Inspired Decoupled Architecture for Efficient Audio Representation Learning
While self-supervised learning (SSL) has revolutionized audio representation, the excessive parameterization and quadratic computational cost of standard Transformers limit their deployment on resource-constrained devices. To address this bottleneck, we propose HEAR (Human-inspired Efficient Audio Representation), a novel decoupled architecture. Inspired by the human cognitive ability to isolate local acoustic features from global context, HEAR splits the processing pipeline into two dedicated modules: an Acoustic Model for local feature extraction and a Task Model for global semantic integration. Coupled with an Acoustic Tokenizer trained via knowledge distillation, our approach enables ...
| Authors | Harunori Kawano, Takeshi Sasaki |
|---|---|
| Primary category | cs.SD |
| Published | 27 March 2026 |
| Last updated at source | 27 March 2026 |
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