Urban sound & sight : Dataset and benchmark for audio-visual urban scene understanding
Resumen:
Automatic audio-visual urban traffic understanding is a growing area of research with many potential applications of value to industry, academia, and the public sector. Yet, the lack of well-curated resources for training and evaluating models to research in this area hinders their development. To address this we present a curated audio-visual dataset, Urban Sound & Sight (Urbansas), developed for investigating the detection and localization of sounding vehicles in the wild. Urbansas consists of 12 hours of unlabeled data along with 3 hours of manually annotated data, including bounding boxes with classes and unique id of vehicles, and strong audio labels featuring vehicle types and indicating off-screen sounds. We discuss the challenges presented by the dataset and how to use its annotations for the localization of vehicles in the wild through audio models.
| 2022 | |
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Location awareness Training Industries Annotations Conferences Signal processing Benchmark testing Audio-visual Urban research Traffic Dataset |
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| Inglés | |
| Universidad de la República | |
| COLIBRI | |
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https://ieeexplore.ieee.org/document/9747644
https://hdl.handle.net/20.500.12008/31397 |
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| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| _version_ | 1807522899299401728 |
|---|---|
| author | Fuentes, Magdalena |
| author2 | Steers, Bea Zinemanas, Pablo Rocamora, Martín Bondi, Luca Wilkins, Julia Shi, Qianyi Hou, Yao Das, Samarjit Serra, Xavier Bello, Juan Pablo |
| author2_role | author author author author author author author author author author |
| author_facet | Fuentes, Magdalena Steers, Bea Zinemanas, Pablo Rocamora, Martín Bondi, Luca Wilkins, Julia Shi, Qianyi Hou, Yao Das, Samarjit Serra, Xavier Bello, Juan Pablo |
| author_role | author |
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| collection | COLIBRI |
| dc.contributor.filiacion.none.fl_str_mv | Fuentes Magdalena, New York University, New York, NY Steers Bea, New York University, New York, NY Zinemanas Pablo, Universitat Pompeu Fabra, Barcelona, Spain Rocamora Martín, Universidad de la República (Uruguay). Facultad de Ingeniería. Bondi Luca, Bosch Research, Pittsburgh, PA, USA Wilkins Julia, New York University, New York, NY Shi Qianyi, New York University, New York, NY Hou Yao, New York University, New York, NY Das Samarjit, Bosch Research, Pittsburgh, PA, USA Serra Xavier, Universitat Pompeu Fabra, Barcelona, Spain Bello Juan Pablo, New York University, New York, NY |
| dc.creator.none.fl_str_mv | Fuentes, Magdalena Steers, Bea Zinemanas, Pablo Rocamora, Martín Bondi, Luca Wilkins, Julia Shi, Qianyi Hou, Yao Das, Samarjit Serra, Xavier Bello, Juan Pablo |
| dc.date.accessioned.none.fl_str_mv | 2022-05-03T12:01:35Z |
| dc.date.available.none.fl_str_mv | 2022-05-03T12:01:35Z |
| dc.date.issued.none.fl_str_mv | 2022 |
| dc.description.abstract.none.fl_txt_mv | Automatic audio-visual urban traffic understanding is a growing area of research with many potential applications of value to industry, academia, and the public sector. Yet, the lack of well-curated resources for training and evaluating models to research in this area hinders their development. To address this we present a curated audio-visual dataset, Urban Sound & Sight (Urbansas), developed for investigating the detection and localization of sounding vehicles in the wild. Urbansas consists of 12 hours of unlabeled data along with 3 hours of manually annotated data, including bounding boxes with classes and unique id of vehicles, and strong audio labels featuring vehicle types and indicating off-screen sounds. We discuss the challenges presented by the dataset and how to use its annotations for the localization of vehicles in the wild through audio models. |
| dc.format.mimetype.es.fl_str_mv | application/pdf |
| dc.identifier.citation.es.fl_str_mv | Fuentes, M., Steers, B., Zinemanas, P. y otros. Urban sound & sight : Dataset and benchmark for audio-visual urban scene understanding [en línea]. EN: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore, 23-27 may, pp 141-145. Piscataway, NJ : IEEE, 2022. DOI 10.1109/ICASSP43922.2022.9747644 |
| dc.identifier.doi.none.fl_str_mv | 10.1109/ICASSP43922.2022.9747644 |
| dc.identifier.uri.none.fl_str_mv | https://ieeexplore.ieee.org/document/9747644 https://hdl.handle.net/20.500.12008/31397 |
| dc.language.iso.none.fl_str_mv | en eng |
| dc.publisher.es.fl_str_mv | IEEE |
| dc.relation.ispartof.es.fl_str_mv | ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore, 23-27 may 2022, pp. 141-145. |
| dc.rights.license.none.fl_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.source.none.fl_str_mv | reponame:COLIBRI instname:Universidad de la República instacron:Universidad de la República |
| dc.subject.es.fl_str_mv | Location awareness Training Industries Annotations Conferences Signal processing Benchmark testing Audio-visual Urban research Traffic Dataset |
| dc.title.none.fl_str_mv | Urban sound & sight : Dataset and benchmark for audio-visual urban scene understanding |
| dc.type.es.fl_str_mv | Ponencia |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/conferenceObject |
| dc.type.version.none.fl_str_mv | info:eu-repo/semantics/publishedVersion |
| description | Automatic audio-visual urban traffic understanding is a growing area of research with many potential applications of value to industry, academia, and the public sector. Yet, the lack of well-curated resources for training and evaluating models to research in this area hinders their development. To address this we present a curated audio-visual dataset, Urban Sound & Sight (Urbansas), developed for investigating the detection and localization of sounding vehicles in the wild. Urbansas consists of 12 hours of unlabeled data along with 3 hours of manually annotated data, including bounding boxes with classes and unique id of vehicles, and strong audio labels featuring vehicle types and indicating off-screen sounds. We discuss the challenges presented by the dataset and how to use its annotations for the localization of vehicles in the wild through audio models. |
| eu_rights_str_mv | openAccess |
| format | conferenceObject |
| id | COLIBRI_b40ec9819e3b28e2609b257d4e269a52 |
| identifier_str_mv | Fuentes, M., Steers, B., Zinemanas, P. y otros. Urban sound & sight : Dataset and benchmark for audio-visual urban scene understanding [en línea]. EN: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore, 23-27 may, pp 141-145. Piscataway, NJ : IEEE, 2022. DOI 10.1109/ICASSP43922.2022.9747644 10.1109/ICASSP43922.2022.9747644 |
| instacron_str | Universidad de la República |
| institution | Universidad de la República |
| instname_str | Universidad de la República |
| language | eng |
| language_invalid_str_mv | en |
| network_acronym_str | COLIBRI |
| network_name_str | COLIBRI |
| oai_identifier_str | oai:colibri.udelar.edu.uy:20.500.12008/31397 |
| publishDate | 2022 |
| reponame_str | COLIBRI |
| repository.mail.fl_str_mv | mabel.seroubian@seciu.edu.uy |
| repository.name.fl_str_mv | COLIBRI - Universidad de la República |
| repository_id_str | 4771 |
| rights_invalid_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| spelling | Fuentes Magdalena, New York University, New York, NYSteers Bea, New York University, New York, NYZinemanas Pablo, Universitat Pompeu Fabra, Barcelona, SpainRocamora Martín, Universidad de la República (Uruguay). Facultad de Ingeniería.Bondi Luca, Bosch Research, Pittsburgh, PA, USAWilkins Julia, New York University, New York, NYShi Qianyi, New York University, New York, NYHou Yao, New York University, New York, NYDas Samarjit, Bosch Research, Pittsburgh, PA, USASerra Xavier, Universitat Pompeu Fabra, Barcelona, SpainBello Juan Pablo, New York University, New York, NY2022-05-03T12:01:35Z2022-05-03T12:01:35Z2022Fuentes, M., Steers, B., Zinemanas, P. y otros. Urban sound & sight : Dataset and benchmark for audio-visual urban scene understanding [en línea]. EN: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore, 23-27 may, pp 141-145. Piscataway, NJ : IEEE, 2022. DOI 10.1109/ICASSP43922.2022.9747644https://ieeexplore.ieee.org/document/9747644https://hdl.handle.net/20.500.12008/3139710.1109/ICASSP43922.2022.9747644Automatic audio-visual urban traffic understanding is a growing area of research with many potential applications of value to industry, academia, and the public sector. Yet, the lack of well-curated resources for training and evaluating models to research in this area hinders their development. To address this we present a curated audio-visual dataset, Urban Sound & Sight (Urbansas), developed for investigating the detection and localization of sounding vehicles in the wild. Urbansas consists of 12 hours of unlabeled data along with 3 hours of manually annotated data, including bounding boxes with classes and unique id of vehicles, and strong audio labels featuring vehicle types and indicating off-screen sounds. We discuss the challenges presented by the dataset and how to use its annotations for the localization of vehicles in the wild through audio models.Submitted by Ribeiro Jorge (jribeiro@fing.edu.uy) on 2022-04-28T23:05:08Z No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) FSZRBWSHDSB22.pdf: 5680707 bytes, checksum: 30cc85dcb22591cf55d406360f46bb52 (MD5)Approved for entry into archive by Machado Jimena (jmachado@fing.edu.uy) on 2022-05-02T20:41:38Z (GMT) No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) FSZRBWSHDSB22.pdf: 5680707 bytes, checksum: 30cc85dcb22591cf55d406360f46bb52 (MD5)Made available in DSpace by Luna Fabiana (fabiana.luna@seciu.edu.uy) on 2022-05-03T12:01:35Z (GMT). No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) FSZRBWSHDSB22.pdf: 5680707 bytes, checksum: 30cc85dcb22591cf55d406360f46bb52 (MD5) Previous issue date: 2022application/pdfenengIEEEICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore, 23-27 may 2022, pp. 141-145.Las obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad de la República.(Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)Location awarenessTrainingIndustriesAnnotationsConferencesSignal processingBenchmark testingAudio-visualUrban researchTrafficDatasetUrban sound & sight : Dataset and benchmark for audio-visual urban scene understandingPonenciainfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaFuentes, MagdalenaSteers, BeaZinemanas, PabloRocamora, MartínBondi, LucaWilkins, JuliaShi, QianyiHou, YaoDas, SamarjitSerra, XavierBello, Juan PabloProcesamiento de SeñalesProcesamiento de AudioLICENSElicense.txtlicense.txttext/plain; charset=utf-84267http://localhost:8080/xmlui/bitstream/20.500.12008/31397/5/license.txt6429389a7df7277b72b7924fdc7d47a9MD55CC-LICENSElicense_urllicense_urltext/plain; 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- Universidad de la Repúblicafalse |
| spellingShingle | Urban sound & sight : Dataset and benchmark for audio-visual urban scene understanding Fuentes, Magdalena Location awareness Training Industries Annotations Conferences Signal processing Benchmark testing Audio-visual Urban research Traffic Dataset |
| status_str | publishedVersion |
| title | Urban sound & sight : Dataset and benchmark for audio-visual urban scene understanding |
| title_full | Urban sound & sight : Dataset and benchmark for audio-visual urban scene understanding |
| title_fullStr | Urban sound & sight : Dataset and benchmark for audio-visual urban scene understanding |
| title_full_unstemmed | Urban sound & sight : Dataset and benchmark for audio-visual urban scene understanding |
| title_short | Urban sound & sight : Dataset and benchmark for audio-visual urban scene understanding |
| title_sort | Urban sound & sight : Dataset and benchmark for audio-visual urban scene understanding |
| topic | Location awareness Training Industries Annotations Conferences Signal processing Benchmark testing Audio-visual Urban research Traffic Dataset |
| url | https://ieeexplore.ieee.org/document/9747644 https://hdl.handle.net/20.500.12008/31397 |
