Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
AudioGenX: Explainability on Text-to-Audio Generative Models
Authors: Hyunju Kang, Geonhee Han, Yoonjae Jeong, Hogun Park
AAAI 2025 | Venue PDF | LLM Run Details | Input Tokens: 14,821 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,702 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the effectiveness of Audio Gen X in producing faithful explanations, benchmarked against existing methods using novel evaluation metrics specifically designed for audio generation tasks. |
| Researcher Affiliation | Collaboration | Hyunju Kang1*, Geonhee Han1*, Yoonjae Jeong2, Hogun Park1 1Department of Artificial Intelligence, Sungkyunkwan University, Suwon, Republic of Korea 2Audio AI Lab, NCSOFT, Seongnam, Republic of Korea EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Audio Gen X |
| Open Source Code | Yes | Our code is available at the following link 1. 1https://github.com/hjkng/audiogenX |
| Open Datasets | Yes | We use Audio Caps (Kim et al. 2019) as the source of textual prompts. |
| Dataset Splits | Yes | For hyperparameter tuning, we select 100 validation captions, while the test dataset consists of 1,000 randomly selected captions. |
| Hardware Specification | No | The paper mentions memory usage in MB for efficiency analysis, but no specific hardware details (e.g., GPU/CPU models, RAM size) used for running the experiments are provided. |
| Software Dependencies | No | The paper mentions using the Adam optimizer, but no specific version numbers for software components like Python, PyTorch, or CUDA are provided. |
| Experiment Setup | Yes | The Explainer is trained for 50 epochs with a learning rate as 10 3 using the Adam optimizer. Hyperparameters are set as α = 1 10 3 and β = 1 10 1 as coefficients for the explanation objective function. |