Organizer of Task 4 “Sound event detection in domestic environments”. Accountable for code development, dataset development, paper reviews and system submissions as well as providing technical support to participants via email and DCASE forum.
This task is the follow-up to DCASE 2018 task 4. The task evaluates systems for the large-scale detection of sound events using weakly labeled data (without timestamps). The target of the systems is to provide not only the event class but also the event time boundaries given that multiple events can be present in an audio recording. The challenge of exploring the possibility to exploit a large amount of unbalanced and unlabeled training data together with a small weakly annotated training set to improve system performance remains but an additional training set with strongly annotated synthetic data is provided. The labels in all the annotated subsets are verified and can be considered as reliable. An additional scientific question this task is aiming to investigate is whether we really need real but partially and weakly annotated data or is using synthetic data sufficient? or do we need both?