  In this paper, a special decision surface for the weakly-supervised sound
event detection (SED) and a disentangled feature (DF) for the multi-label
problem in polyphonic SED are proposed. We approach SED as a multiple instance
learning (MIL) problem and utilize a neural network framework with a pooling
module to solve it. General MIL approaches include two kinds: the
instance-level approaches and embedding-level approaches. We present a method
of generating instance-level probabilities for the embedding level approaches
which tend to perform better than the instance-level approaches in terms of
bag-level classification but can not provide instance-level probabilities in
current approaches. Moreover, we further propose a specialized decision surface
(SDS) for the embedding-level attention pooling. We analyze and explained why
an embedding-level attention module with SDS is better than other typical
pooling modules from the perspective of the high-level feature space. As for
the problem of the unbalanced dataset and the co-occurrence of multiple
categories in the polyphonic event detection task, we propose a DF to reduce
interference among categories, which optimizes the high-level feature space by
disentangling it based on class-wise identifiable information and obtaining
multiple different subspaces. Experiments on the dataset of DCASE 2018 Task 4
show that the proposed SDS and DF significantly improve the detection
performance of the embedding-level MIL approach with an attention pooling
module and outperform the first place system in the challenge by 6.6 percentage
points.
