  Large-scale sound recognition data sets typically consist of acoustic
recordings obtained from multimedia libraries. As a consequence, modalities
other than audio can often be exploited to improve the outputs of models
designed for associated tasks. Frequently, however, not all contents are
available for all samples of such a collection: For example, the original
material may have been removed from the source platform at some point, and
therefore, non-auditory features can no longer be acquired.
  We demonstrate that a multi-encoder framework can be employed to deal with
this issue by applying this method to attention-based deep learning systems,
which are currently part of the state of the art in the domain of sound
recognition. More specifically, we show that the proposed model extension can
successfully be utilized to incorporate partially available visual information
into the operational procedures of such networks, which normally only use
auditory features during training and inference. Experimentally, we verify that
the considered approach leads to improved predictions in a number of evaluation
scenarios pertaining to audio tagging and sound event detection. Additionally,
we scrutinize some properties and limitations of the presented technique.
