A Human-Inspired Decoupled Architecture for Efficient Audio Representation Learning

1. 1Identify the limitations of standard Transformers in audio representation learning
2. 2Propose a decoupled architecture inspired by human cognitive abilities
3. 3Implement the HEAR architecture to reduce parameterization and computational cost
4. 4Evaluate the performance of HEAR on various audio representation tasks

AI engineers and researchers working on audio representation learning can benefit from this architecture, as it enables efficient deployment on resource-constrained devices

💡 Decoupling local acoustic feature extraction from global context processing can improve efficiency in audio representation learning

💡 Human-inspired architecture for efficient audio representation learning reduces parameterization and computational cost