Cognitive Load Estimation in Music Education via Multi-Channel Auditory Processing

ACME: Cognitive Load Estimation in Music Education via Multi-Channel Auditory Processing Official implementation of the paper: Cognitive Load Estimation in Music Education via Multi-Channel Auditory Processing Overview This repository contains the implementation of ACME (Auditory Cognitive Manifold Estimator), a framework for cognitive load estimation in music education based on multi-channel auditory processing. The proposed framework integrates auditory signal processing, cognitive modeling, and probabilistic reasoning to estimate learners' cognitive load in complex auditory environments. ACME is designed to improve robustness, interpretability, and adaptability compared with conventional cognitive load estimation methods. Framework The proposed framework consists of three major components: - Constraint Guided Feature Decomposer - Extracts informative temporal and spectral auditory representations. - Performs structured feature decomposition under domain-specific constraints. - Agent Driven Auditory Planner - Models dynamic auditory interactions. - Learns cognitive-aware auditory representations. - Probabilistic Load Regularizer - Introduces probabilistic reasoning into cognitive load estimation. - Improves robustness under noisy and uncertain auditory conditions. Additionally, the framework employs a **Probabilistic Auditory Cognitive Refinement** strategy with manifold adaptation scheduling for enhanced generalization. Features - Multi-channel auditory signal processing- Cognitive manifold representation learning- Probabilistic cognitive load estimation- Modular architecture- Scalable framework for music education applications Repository Structure .├── datasets/ # Dataset preparation├── models/ # ACME implementation├── modules/│ ├── feature_decomposer.py│ ├── auditory_planner.py│ └── load_regularizer.py├── training/│ ├── train.py│ └── evaluate.py├── utils/├── configs/├── figures/├── checkpoints/└── README.md Installation Clone the repository: bashgit clone https://github.com/yourname/ACME.gitcd ACME Install dependencies: bashpip install -r requirements.txt Training Run the training script: bashpython training/train.py Evaluation Evaluate a trained model: bashpython training/evaluate.py Experimental Results The proposed ACME framework consistently outperforms existing methods on multiple benchmark datasets for cognitive load estimation in music education. Main advantages include: - Higher estimation accuracy- Improved robustness- Better generalization- Probabilistic uncertainty modeling- Modular and interpretable architecture Citation If you find this repository useful, please cite: bibtex@article{chen2025acme, title={Cognitive Load Estimation in Music Education via Multi-Channel Auditory Processing}, author={Chen, Yangyijie}, journal={Frontiers}, year={2025}} License This project is released under the MIT License. Acknowledgements This work was supported by the research project: Innovative Research on Ideological and Political Education in Art Vocational Colleges Based on AI Research Project on Ideological and Political Education in Hunan Province (2025). Contact Yangyijie Chen Hunan Mass Media Vocational and Technical College Email: [e-mail]