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We propose a new task for sound event detection (SED): sound event triage(SET). The goal of SET is to detect an arbitrary number of high-priority eventclasses while allowing misdetections of low-priority event classes where thepriority is given for each event class. In conventional methods of SED fortargeting a specific sound event class, it is only possible to give priority toa single event class. Moreover, the level of priority is not adjustable, i.e,the conventional methods can use only types of target event class such asone-hot vector, as inputs. To flexibly control much information on the targetevent, the proposed SET exploits not only types of target sound but also theextent to which each target sound is detected with priority. To implement thedetection of events with priority, we propose class-weighted training, in whichloss functions and the network are stochastically weighted by the priorityparameter of each class. As this is the first paper on SET, we particularlyintroduce an implementation of single target SET, which is a subtask of SET.Results of the experiments using the URBAN-SED dataset show that the proposedmethod of single target SET outperforms the conventional SED method by 8.70,6.66, and 6.09 percentage points for ``air_conditioner,'' ``car_horn,'' and``street_music,'' respectively, in terms of the intersection-based F-score. Forthe average score of classes, the proposed methods increase theintersection-based F-score by up to 3.37 percentage points compared with theconventional SED and other target-class-conditioned models.

Sound Event Triage: Detecting Sound Events Considering Priority of Classes