  The lack of strong labels has severely limited the state-of-the-art fully
supervised audio tagging systems to be scaled to larger dataset. Meanwhile,
audio-visual learning models based on unlabeled videos have been successfully
applied to audio tagging, but they are inevitably resource hungry and require a
long time to train. In this work, we propose a light-weight, multimodal
framework for environmental audio tagging. The audio branch of the framework is
a convolutional and recurrent neural network (CRNN) based on multiple instance
learning (MIL). It is trained with the audio tracks of a large collection of
weakly labeled YouTube video excerpts; the video branch uses pretrained
state-of-the-art image recognition networks and word embeddings to extract
information from the video track and to map visual objects to sound events.
Experiments on the audio tagging task of the DCASE 2017 challenge show that the
incorporation of video information improves a strong baseline audio tagging
system by 5.3\% absolute in terms of $F_1$ score. The entire system can be
trained within 6~hours on a single GPU, and can be easily carried over to other
audio tasks such as speech sentimental analysis.
