  Sound event detection (SED) entails two subtasks: recognizing what types of
sound events are present in an audio stream (audio tagging), and pinpointing
their onset and offset times (localization). In the popular multiple instance
learning (MIL) framework for SED with weak labeling, an important component is
the pooling function. This paper compares five types of pooling functions both
theoretically and experimentally, with special focus on their performance of
localization. Although the attention pooling function is currently receiving
the most attention, we find the linear softmax pooling function to perform the
best among the five. Using this pooling function, we build a neural network
called TALNet. It is the first system to reach state-of-the-art audio tagging
performance on Audio Set, while exhibiting strong localization performance on
the DCASE 2017 challenge at the same time.
