  Considering that acoustic scenes and sound events are closely related to each
other, in some previous papers, a joint analysis of acoustic scenes and sound
events utilizing multitask learning (MTL)-based neural networks was proposed.
In conventional methods, a strongly supervised scheme is applied to sound event
detection in MTL models, which requires strong labels of sound events in model
training; however, annotating strong event labels is quite time-consuming. In
this paper, we thus propose a method for the joint analysis of acoustic scenes
and sound events based on the MTL framework with weak labels of sound events.
In particular, in the proposed method, we introduce the multiple-instance
learning scheme for weakly supervised training of sound event detection and
evaluate four pooling functions, namely, max pooling, average pooling,
exponential softmax pooling, and attention pooling. Experimental results
obtained using parts of the TUT Acoustic Scenes 2016/2017 and TUT Sound Events
2016/2017 datasets show that the proposed MTL-based method with weak labels
outperforms the conventional single-task-based scene classification and event
detection models with weak labels in terms of both the scene classification and
event detection performances.
