Naranjo-Alcaraz, J.; Perez-Castanos, S.; Zuccarello, P.; Torres, AM.; López Monfort, JJ.; Ferri, FJ.; Cobos, M. (2022). An Open-Set Recognition and Few-Shot Learning Dataset for Audio Event Classification in Domestic Environments. Pattern Recognition Letters. 164:40-45. https://doi.org/10.1016/j.patrec.2022.10.019
Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/197443
Title:
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An Open-Set Recognition and Few-Shot Learning Dataset for Audio Event Classification in Domestic Environments
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Author:
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Naranjo-Alcaraz, Javier
Perez-Castanos, Sergi
Zuccarello, Pedro
Torres, Ana M.
López Monfort, José Javier
Ferri, Francesc J.
Cobos, Maximo
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UPV Unit:
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Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros de Telecomunicación - Escola Tècnica Superior d'Enginyers de Telecomunicació
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Issued date:
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Abstract:
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[EN] The problem of training with a small set of positive samples is known as few-shot learning (FSL). It is widely known that traditional deep learning algorithms usually show very good performance when trained with large ...[+]
[EN] The problem of training with a small set of positive samples is known as few-shot learning (FSL). It is widely known that traditional deep learning algorithms usually show very good performance when trained with large datasets. However, in many applications, it is not possible to obtain such a high num- ber of samples. This paper deals with the application of FSL to the detection of specific and intentional acoustic events given by different types of sound alarms, such as door bells or fire alarms, using a limited number of samples. These sounds typically occur in domestic environments where many events corre- sponding to a wide variety of sound classes take place. Therefore, the detection of such alarms in a practi- cal scenario can be considered an open-set recognition (OSR) problem. To address the lack of a dedicated public dataset for audio FSL, researchers usually make modifications on other available datasets. This pa- per is aimed at providing the audio recognition community with a carefully annotated dataset1 for FSL in an OSR context comprised of 1360 clips from 34 classes divided into pattern sounds and unwanted sounds. To facilitate and promote research on this area, results with state-of-the-art baseline systems based on transfer learning are also presented.
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Subjects:
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Audio Dataset
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Classification
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Few-Shot Learning
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Machine Listening
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Open-set Recognition
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Sound Processing
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Copyrigths:
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Reconocimiento (by)
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Source:
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Pattern Recognition Letters. (issn:
0167-8655
)
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DOI:
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10.1016/j.patrec.2022.10.019
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Publisher:
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Elsevier
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Publisher version:
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https://doi.org/10.1016/j.patrec.2022.10.019
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Project ID:
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info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-097045-B-C21/ES/AUDIO ESPACIAL INTELIGENTE: ANALISIS Y MONITORIZACION/
...[+]
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-097045-B-C21/ES/AUDIO ESPACIAL INTELIGENTE: ANALISIS Y MONITORIZACION/
info:eu-repo/grantAgreement/GVA//AICO%2F2020%2F154//SONO-TEMATIC Técnicas de aprendizaje automático aplicadas al análisis computacional de escenas sonoras y la síntesis de entornos inmersivos/
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-097045-B-C22/ES/AUDIO ESPACIAL INTELIGENTE: SINTESIS Y PERSONALIZACION/
info:eu-repo/grantAgreement/MCIU//DIN2018-009982//Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/
info:eu-repo/grantAgreement/MICINN//TED2021-131003B-C21//Proyectos Estratégicos Orientados a la Transición Ecológica y a la Transición Digital. Convocatoria 2021/
info:eu-repo/grantAgreement/EC/H2020/779158/EU
info:eu-repo/grantAgreement/MICINN//PTQ-17-09106//Ayudas para contratos Torres Quevedo (PTQ)/
info:eu-repo/grantAgreement/MICINN//TED2021-131003B-C22//Proyectos Estratégicos Orientados a la Transición Ecológica y a la Transición Digital. Convocatoria 2021/
info:eu-repo/grantAgreement/CIUCSD//AEST%2F2020%2F012//Subvenciones para la realización de estancias de personal investigador doctor en empresas de la Comunitat Valenciana/
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Thanks:
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This work was supported by the EU Horizon 2020 programme [grant No 779158] . Grants DIN2018-009982, PTQ-17-09106, RTI2018-097045-B-C21/C22 funded by MCIN/AEI/10.13039/50110 0 011033, the latter also by "ERDF A way of making ...[+]
This work was supported by the EU Horizon 2020 programme [grant No 779158] . Grants DIN2018-009982, PTQ-17-09106, RTI2018-097045-B-C21/C22 funded by MCIN/AEI/10.13039/50110 0 011033, the latter also by "ERDF A way of making Europe ". Grants TED2021-131003B-C21/C22 funded by MCIN/AEI/10.13039/50110 0 011033 and by the "EU Union NextGenerationEU/PRTR ". Grants AICO/2020/154 and AEST/2020/012, funded by GVA. The authors acknowledge also the Artemisa computer resources funded by the EU ERDF and Comunitat Valenciana, and the technical support of IFIC (CSIC-UV) . Authors J. Naranjo, S. Perez and P. Zuccarello were working at Visualfy when this work was done, but they are now with the Instituto Tecnologico de Informatica (ITI) , Tyris AI and ITI, respectively.
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Type:
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Artículo
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