  Access to large corpora with strongly labelled sound events is expensive and
difficult in engineering applications. Much research turns to address the
problem of how to detect both the types and the timestamps of sound events with
weak labels that only specify the types. This task can be treated as a multiple
instance learning (MIL) problem, and the key to it is the design of a pooling
function. In this paper, we propose an adaptive power pooling function which
can automatically adapt to various sound sources. On two public datasets, the
proposed power pooling function outperforms the state-of-the-art linear softmax
pooling on both coarsegrained and fine-grained metrics. Notably, it improves
the event-based F1 score (which evaluates the detection of event onsets and
offsets) by 11.4% and 10.2% relative on the two datasets. While this paper
focuses on sound event detection applications, the proposed method can be
applied to MIL tasks in other domains.
