  State-of-the-art audio event detection (AED) systems rely on supervised
learning using strongly labeled data. However, this dependence severely limits
scalability to large-scale datasets where fine resolution annotations are too
expensive to obtain. In this paper, we propose a small-footprint multiple
instance learning (MIL) framework for multi-class AED using weakly annotated
labels. The proposed MIL framework uses audio embeddings extracted from a
pre-trained convolutional neural network as input features. We show that by
using audio embeddings the MIL framework can be implemented using a simple DNN
with performance comparable to recurrent neural networks.
  We evaluate our approach by training an audio tagging system using a subset
of AudioSet, which is a large collection of weakly labeled YouTube video
excerpts. Combined with a late-fusion approach, we improve the F1 score of a
baseline audio tagging system by 17%. We show that audio embeddings extracted
by the convolutional neural networks significantly boost the performance of all
MIL models. This framework reduces the model complexity of the AED system and
is suitable for applications where computational resources are limited.
