MID-FiLD: MIDI Dataset for Fine-Level Dynamics
Authors: Jesung Ryu, Seungyeon Rhyu, Hong-Gyu Yoon, Eunchong Kim, Ju Young Yang, Taehyun Kim
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Based on our dataset, we conduct the following analysis and experiments to demonstrate its excellence. We conducted an ablation study for objective evaluation, successively removing items in metadata as a model input. |
| Researcher Affiliation | Collaboration | Jesung Ryu1, Seungyeon Rhyu1, Hong-Gyu Yoon1, Eunchong Kim1, Ju Young Yang2, Taehyun Kim1* 1Pozalabs, Republic of Korea 2Duke University, United States |
| Pseudocode | No | The paper describes the input encoding approach and problem definition, but it does not include any explicitly labeled pseudocode blocks or algorithms. |
| Open Source Code | No | The paper does not provide any direct links to a code repository or an explicit statement about the public release of the source code for the methodology described. |
| Open Datasets | No | The paper introduces a new dataset, MID-Fi LD, but does not provide any specific link (URL, DOI, or repository name) or an explicit statement confirming its public availability for download. |
| Dataset Splits | Yes | We divided 4,422 MID-Fi LD samples into a ratio of 8:1:1 for training, validation and test sets respectively, leaving about 10% of the data for test performance measurements (i.e., 3, 547 : 443 : 432). |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models (e.g., NVIDIA A100, RTX 2080 Ti), CPU models, or memory configurations used for running the experiments. |
| Software Dependencies | No | The paper mentions using deep learning models and a support vector machine but does not specify any software versions for libraries, frameworks (e.g., PyTorch 1.x, TensorFlow 2.x), or programming languages (e.g., Python 3.x). |
| Experiment Setup | Yes | We used 9-fold cross-validation for training the SVM (C = 10, γ = 1) following the division ratio of the dataset. |