MusER: Musical Element-Based Regularization for Generating Symbolic Music with Emotion
Authors: Shulei Ji, Xinyu Yang
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that Mus ER outperforms the state-of-the-art models for generating emotional music in both objective and subjective evaluation. Experiments showed that Mus ER outperformed prior methods for generating emotional music in both objective music metrics and emotional expression. |
| Researcher Affiliation | Academia | Shulei Ji, Xinyu Yang School of Computer Science and Technology, Xi an Jiaotong University, Xi an 710049, P. R. China taylorji@stu.xjtu.edu.cn, yxyphd@mail.xjtu.edu.cn |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We provide the generated music examples and code at a Git Hub repo: https://github.com/Tayjsl97/MusER. |
| Open Datasets | Yes | Dataset. Two datasets are used in this paper to train the proposed model. The first one is the Pop1k7 dataset1 (Hsiao et al. 2021), which contains 1748 piano covers of pop songs automatically transcribed by a piano transcription model (Hawthorne et al. 2018) and converted into MIDI files. The second one is the EMOPIA dataset2 (Hung et al. 2021), a multi-modal database focusing on perceived emotion in pop piano music. This dataset comprises 1087 music clips from 387 piano solo performances and clip-level emotion labels annotated by dedicated annotators. |
| Dataset Splits | No | The paper does not provide specific details regarding training, validation, and test dataset splits (e.g., percentages, sample counts, or citations to predefined splits). |
| Hardware Specification | No | The paper does not provide specific hardware details such as CPU/GPU models, memory specifications, or types of computing resources used for experiments. |
| Software Dependencies | No | The paper mentions using the 'Muspy library (Dong et al. 2020)' but does not specify version numbers for Muspy or any other key software components like Python, PyTorch, or TensorFlow. |
| Experiment Setup | Yes | Following (Hung et al. 2021), we set the length of the token sequence to 1024 for both datasets and apply specific sampling policies (temperature sampling and nucleus sampling (Holtzman et al. 2020)) for different elements. |