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.

本文讨论了声音事件检测中弱标注的多实例学习 (MIL) 框架中池化函数的 5 种类型的理论和实验比较，尤其是针对它们的定位性能。结果发现，线性 softmax 池化函数表现最佳，于是作者构建了一个名为 TALNet 的神经网络，在 Audio Set 上达到了最先进的音频标记性能，同时在 DCASE2017 挑战赛上展现了强大的定位性能。