Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
MHANet: Multi-scale Hybrid Attention Network for Auditory Attention Detection
Authors: Lu Li, Cunhang Fan, Hongyu Zhang, Jingjing Zhang, Xiaoke Yang, Jian Zhou, Zhao Lv
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that the proposed MHANet achieves state-of-the-art performance with fewer trainable parameters across three datasets, 3 times lower than that of the most advanced model. |
| Researcher Affiliation | Academia | Anhui Province Key Laboratory of Multimodal Cognitive Computation, School of Computer Science and Technology, Anhui University, Hefei, 230601, China EMAIL EMAIL |
| Pseudocode | No | The paper describes the MHANet architecture and its components (MHA, MTA, MGA, STC) using textual descriptions and mathematical formulations, but it does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at: https://github.com/fchest/MHANet. |
| Open Datasets | Yes | In this paper, we conduct extensive experiments on three publicly available datasets, namely KUL [Das et al., 2016; Das et al., 2019], DTU [Fuglsang et al., 2017; Fuglsang et al., 2018] and AVED [Fan et al., 2024b], as shown in Table 1. |
| Dataset Splits | Yes | The dataset is initially divided into training, validation, and test sets in a ratio of 8:1:1. For each subject in the KUL dataset, we allocate 4,600 decision windows for training, 576 for validation, and 576 for testing. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running the experiments. |
| Software Dependencies | No | The paper mentions the use of an "Adam W optimizer" but does not specify versions for any programming languages or libraries (e.g., Python, PyTorch, TensorFlow, CUDA). |
| Experiment Setup | Yes | The training process uses a batch size of 32, with a maximum of 100 epochs. An early stopping strategy is employed, halting training if there is no decrease in the validation set s loss function value for 15 consecutive epochs. The model is trained using the Adam W optimizer with a learning rate of 5e-3 and weight decay of 3e-4. |