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This repository contains the development data of task 5 of the DCASE 2018 challenge. The dataset is a derivative of the SINS database. The SINS database contains a continuous recording of one person living in a vacation home over a period of one week. The recordings were manually annotated on daily activity level: "Cooking", "Dishwashing", "Eating", "Social activity (visit, phone call)", "Vacuum cleaning", "Watching TV", "Working", "Presence" and "Absence". More information can be found on (please cite this paper when using the dataset): Dekkers G., Lauwereins S., Thoen B., Adhana M., Brouckxon H., Van den Bergh B., van Waterschoot T., Vanrumste B., Verhelst M., Karsmakers P. (2017). The SINS database for detection of daily activities in a home environment using an Acoustic Sensor Network. Detection and Classification of Acoustic Scenes and Events 2017 (accepted). DCASE Workshop. München, Germany, 16-17 November 2017. The derivative of the SINS database, 'DCASE 2018 – Task 5 development dataset' consists of data collected by 4 microphone arrays in the combined living room and kitchen area. The continuous recordings were split into audio segments of 10s. These audio segments are provided as individual files along with the ground truth. In total 75484 segments are made available, leading to approximately 210 hours of data. More information about the challenge and the specific dataset can be found here. By accessing or using this database, the user accepts the provided EULA (available in DCASE2018-task5-dev.doc.zip).
{"references": ["Dekkers G., Lauwereins S., Thoen B., Adhana M., Brouckxon H., Van den Bergh B., van Waterschoot T., Vanrumste B., Verhelst M., Karsmakers P. (2017). The SINS database for detection of daily activities in a home environment using an Acoustic Sensor Network. Detection and Classification of Acoustic Scenes and Events 2017 (accepted). DCASE Workshop. M\u00fcnchen, Germany, 16-17 November 2017."]}
DCASE, machine learning, audio, activity of the daily living, home environment, multi-channel
DCASE, machine learning, audio, activity of the daily living, home environment, multi-channel
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