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

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.
Grant RTI2018-097045-B-C21/C22 funded by MCIN/AEI/10.13039/501100011033 and “ERDF A way of making Europe
Grant TED2021-131003B-C21/C22 funded by MCIN/AEI/10.13039/501100011033 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).
Bibliographic reference
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.