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An Open-Set Recognition and Few-Shot Learning Dataset for Audio Event Classification in Domestic Environments

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An Open-Set Recognition and Few-Shot Learning Dataset for Audio Event Classification in Domestic Environments

dc.contributor.author	Naranjo Alcázar, Javier
dc.contributor.author	Perez Castanos, Sergi
dc.contributor.author	Zuccarello, Pedro
dc.contributor.author	Torres, Ana M.
dc.contributor.author	Lopez, Jose J.
dc.contributor.author	Ferri Carreres, Francisco Javier
dc.contributor.author	Cobos, Maximo
dc.date.accessioned	2023-06-16T08:46:28Z
dc.date.available	2023-06-17T04:45:06Z
dc.date.issued	2022	es_ES
dc.identifier.citation	Naranjo-Alcazar, J., Perez-Castanos, S., Zuccarello, P., Torres, A. M., Lopez, J. J., Ferri, F. J., & 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.	es_ES
dc.identifier.uri	https://hdl.handle.net/10550/88332
dc.description.abstract	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 number 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 corresponding to a wide variety of sound classes take place. Therefore, the detection of such alarms in a practical 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 paper is aimed at providing the audio recognition community with a carefully annotated dataset 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.	es_ES
dc.language.iso	en	es_ES
dc.publisher	Elsevier	es_ES
dc.subject	audio dataset	es_ES
dc.subject	classification	es_ES
dc.subject	few-shot learning	es_ES
dc.subject	machine listening	es_ES
dc.subject	open-set recognition	es_ES
dc.subject	sound processing	es_ES
dc.title	An Open-Set Recognition and Few-Shot Learning Dataset for Audio Event Classification in Domestic Environments	es_ES
dc.type	journal article	es_ES
dc.subject.unesco	UNESCO::CIENCIAS TECNOLÓGICAS	es_ES
dc.identifier.doi	10.1016/j.patrec.2022.10.019	es_ES
dc.accrualmethod	CI	es_ES
dc.embargo.terms	0 days	es_ES
dc.type.hasVersion	VoR	es_ES
dc.rights.accessRights	open access	es_ES