The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
ShipsEar: An underwater vessel noise database
Open accessAbstract
Abstract There is a manifest shortage of audio databases available to underwater acoustics researchers. With the aim of palliating this situation, ShipsEar, a database of underwater recordings of ship and boat sounds, has been made available to the research community at http://atlanttic.uvigo.es/underwaternoise/ . The database is currently composed of 90 records representing sounds from 11 vessel types. It includes detailed information on technical aspects of the recordings and environmental and other conditions during acquisition. To demonstrate the usefulness of ShipsEar, a vessel classifier was developed, based on cepstral coefficients and Gaussian mixture models. It was tested on a subset of ShipsEar database in which the original 11 vessel types were merged into 4 vessel size classes. The system yielded an overall classification rate of 75.4%, and 100% accuracy in detecting vessel presence. ShipsEar is potentially useful for the development and testing of applications based on processing underwater vessel sound.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Cited by
Irfan, M., Jiangbin, Z., Ali, S., Iqbal, M., Masood, Z., & Hamid, U. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115270 · Cited by 355
Sensors and AI Techniques for Situational Awareness in Autonomous Ships: A Review
Thombre, S., Zhao, Z., Ramm-Schmidt, H., Vallet Garcia, J. M., Malkamaki, T., Nikolskiy, S., Hammarberg, T., Nuortie, H., H. Bhuiyan, M. Z., Sarkka, S., et al. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3023957 · Cited by 283
Domingos, L. C. F., Santos, P. E., Skelton, P. S. M., Brinkworth, R. S. A., & Sammut, K. (2022) · Sensors · doi:10.3390/s22062181 · Cited by 93
“ShipsEar: An underwater vessel noise database [142] ShipsEar is a database containing underwater recordings of ship and boat sounds, which has 90 recordings of 11 different vessel types.”
MethodDeep convolution stack for waveform in underwater acoustic target recognition
Tian, S., Chen, D., Wang, H., & Liu, J. (2021) · Scientific Reports · doi:10.1038/s41598-021-88799-z · Cited by 86
Shen, S., Yang, H., Li, J., Xu, G., & Sheng, M. (2018) · Entropy · doi:10.3390/e20120990 · Cited by 55
Metadata
- Authors
- Santos-Domínguez, D., Torres-Guijarro, S., Cardenal-López, A., & Pena-Gimenez, A.
- Venue
- Applied Acoustics
- Publisher
- Elsevier BV
- Published
- 2016-12
- Volume
- 113
- Pages
- 64-69
- ISSN
- 0003-682X (ISSN-L)
- OpenAlex
- W2466903746
- Cited by
- 495
- References
- 1
- FWCI
- 4.83
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: ShipsEar: An underwater vessel noise database. https://app.cochat.ai/papers/10.1016/j.apacoust.2016.06.008
- The paper
- Santos-Domínguez, D., Torres-Guijarro, S., Cardenal-López, A., & Pena-Gimenez, A. (2016). ShipsEar: An underwater vessel noise database. Applied Acoustics. https://doi.org/10.1016/j.apacoust.2016.06.008
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
