SlowGuess/NeurIPS_2023_OCR huggingface dataset:
A: STARSS23 is licensed under MIT License.
- Q: Have any third parties imposed IP-based or other restrictions on the data associated with the instances? If so, please describe these restrictions, and provide a link or other access point to, or otherwise reproduce, any relevant licensing terms, as well as any fees associated with these restrictions.
- Q: Do any export controls or other regulatory restrictions apply to the dataset or to individual instances? If so, please describe these restrictions, and provide a link or other access point to, or otherwise reproduce, any supporting documentation.
Q: Who will be supporting/hosting/maintaining the dataset?
A: Sony Group and Tampere University.
- Q: How can the owner/curator/manager of the dataset be contacted (e.g., email address)?
A: Please contact kazuki.shimada@sony.com and archontis.politis@tuni.fi.
- Q: Is there an erratum? If so, please provide a link or other access point.
A: All changes to the dataset will be announced on https://zenodo.org/record/7880637.
- Q: Will the dataset be updated (e.g., to correct labeling errors, add new instances, delete instances)? If so, please describe how often, by whom, and how updates will be communicated to dataset consumers (e.g., mailing list, GitHub)?
A: Yes, all the updates will be syncsed on the website.
- Q: If the dataset relates to people, are there applicable limits on the retention of the data associated with the instances (e.g., were the individuals in question told that their data would be retained for a fixed period of time and then deleted)? If so, please describe these limits and explain how they will be enforced.
- Q: Will older versions of the dataset continue to be supported/hosted/maintained? If so, please describe how. If not, please describe how its obsolescence will be communicated to dataset consumers.
A: The older dataset versions remain in Zenodo if any changes are made.
- Q: If others want to extend/augment/build on/contribute to the dataset, is there a mechanism for them to do so? If so, please provide a description. Will these contributions be validated/verified? If so, please describe how. If not, why not? Is there a process for communicating/distributing these contributions to dataset consumers? If so, please provide a description.
A: Yes. Others can contact the authors of this paper to describe their proposed extension or contribution. We would discuss their proposed contribution to confirm its validity, and if confirmed, we will release a new version of the dataset on Zenodo and announce it accordingly.
(a) Male speech
(b) Clap
(c) Phone
(d) Laugh
(e) Domestic sounds
(f) Door
(g) Music
(h) Musical instruments
(i)Faucet
(j) Bell
(k) Knock
(1) Overall
Figure 7: The distribution of azimuth and elevation labels. Each point in the scatter figures represents a frame. The top and right figures are stacked histograms of azimuth and elevation.
result
Figure 8: An example of object detection results. While the object detector detects people, the detector does not detect a sink, which is slightly to the right of the center.
(a) Class-aware localization error in FOA format
(b) Class-aware localization recall in FOA format
(c) Location-aware F-score in MIC format
(d) Class-aware localization error in MIC format
(e) Class-aware localization recall in MIC format
Figure 9: SELD performance ( $C = 13$ ) per sound event class in audio-visual and audio-only systems evaluated for the dev-set-test in STASS23. We found that a few classes sometimes had zero localization recall, i.e., no true positive output in these classes. As statistics of such cases are less meaningful in localization error, we do not show bars and put N/A in the figures.