Audio files for: Expressive range characterization of open text-to-audio models (AIIDE 2025)
Audio files for: Expressive range characterization of open text-to-audio models (AIIDE 2025)
Audio files for the paper: Jonathan Morse, Azadeh Naderi, Swen Gaudl, Mark Cartwright, Amy K. Hoover, Mark J. Nelson (2025). Expressive range characterization of open text-to-audio models. In: Proceedings of the 21st AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment. Contents: fig1_samples.zip: Generated audio for the examples in Fig. 1. Two prompts; two models; 100 samples for each. thunder_samples.zip: Generated audio for the running "thunder" example. One prompt; two models; 100 samples for each. Source for Figs. 2-4. esc50_samples.zip: Generated audio for the prompt "Sound of X" for each label X in the ESC-50 environmental audio dataset. Fifty prompts; three models; 100 samples for each. Source for Figs. 5-6 and Table 1. generation_scripts.zip: Python scripts used to generate audio from the three models.
- American University United States
- University of Gothenburg Sweden
- New Jersey Institute of Technology United States
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!