There is a newer version of the record available.

Published June 30, 2018 | Version 1.0.0
Journal article Open

DCASE 2018, Task 5: Monitoring of domestic activities based on multi-channel acoustics - Evaluation dataset

  • 1. KU Leuven

Description

The dataset is a derivative of the SINS dataset and is meant to be used as an evaluation set for the DCASE2018 Task 5 challenge. The development set to be used can be found here. 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 papers when using the dataset):

G. Dekkers, S. Lauwereins, B. Thoen, M. W. Adhana, H. Brouckxon, T. van Waterschoot, B. Vanrumste, M. Verhelst, and P. Karsmakers, “The SINS database for detection of daily activities in a home environment using an acoustic
sensor network,” in Proceedings of the Detection and Classification of Acoustic Scenes and Events 2017 Workshop (DCASE2017), Munich, Germany, November 2017, pp. 32–36.

G. Dekkers, L. Vuegen, T. van Waterschoot, B. Vanrumste, and P. Karsmakers, “DCASE 2018 Challenge - Task 5: Monitoring of domestic activities based on multi-channel acoustics,” KU Leuven, Tech. Rep., July 2018.

The derivative of the SINS database, 'DCASE 2018 – Task 5 evaluation dataset' consists of data collected by 7 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. In total 72972 segments are made available, leading to approximately 200 hours of data. Ground truth will be made available after the challenge results have been made public. Additionally, a filename mapping will be made available that will map the filenames to a filename similar as the development dataset.

More information about the challenge and the specific dataset can be found here. Information solely related to the content of the dataset is available in 'DCASE2018-task5-eval.doc.zip'.

By accessing or using this database, the user accepts the provided EULA (available in DCASE2018-task5-eval.doc.zip).

Files

DCASE2018-task5-eval.audio.1.zip

Files (45.4 GB)

Name Size Download all
md5:0df319839c6ca49e1183a6bdda878105
2.0 GB Preview Download
md5:0c619203edc5054c397a53680c401206
2.0 GB Preview Download
md5:5e30ccf28f3b314eceb852926530d183
2.0 GB Preview Download
md5:b12c244e646f70b9318feaba1b62bf18
2.0 GB Preview Download
md5:7f04c5471e102a261671122173bb3faa
2.0 GB Preview Download
md5:4e1d4800d106e1167d5a746f8581fbee
2.0 GB Preview Download
md5:810e1bf859931063c4dce8eb3d80de18
2.0 GB Preview Download
md5:2d858a337a090bf64b69ed6c8a1ffa41
2.0 GB Preview Download
md5:e0541f41cf615951c94fba9aef302eac
2.0 GB Preview Download
md5:418348dd851a320bc003f7c11ca7d17f
2.0 GB Preview Download
md5:b9ef08ba8a09f4ca056d3a3545a1a00b
2.0 GB Preview Download
md5:c4128c29b2ec7ca3a0a640160cea45e9
2.0 GB Preview Download
md5:6c75d2bdb778fc54f1a902300a56e969
2.0 GB Preview Download
md5:ccc5de1cac56a1f3835772aa5fd7613d
2.0 GB Preview Download
md5:ece9169ada4f46cf4e7969dc6cdcc52c
2.0 GB Preview Download
md5:9fda3aa8df12b57a57b0142fd5642a44
1.4 GB Preview Download
md5:367add49b9dad7e4823d12ff17f248ac
2.0 GB Preview Download
md5:aa2a74ff766bba3c45775e73f686bf9c
2.0 GB Preview Download
md5:845637aa439da622ef5529b1f081c235
2.0 GB Preview Download
md5:23dc7375c17f504f602bd293891a0812
2.0 GB Preview Download
md5:391845ae00c20f94d8c2e03d5016cff5
2.0 GB Preview Download
md5:98c9421e99c241e1d96baaffaa7f9848
2.0 GB Preview Download
md5:ffc5be57c89ca1a8480b2124ab650126
2.0 GB Preview Download
md5:46bb9533c4ac74f65a959fa4c963d25f
60.8 kB Preview Download
md5:5256ade2443e80dc11ae8e816edaf664
327.2 kB Preview Download

Additional details

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. München, Germany, 16-17 November 2017.