  Acoustic events often have a visual counterpart. Knowledge of visual
information can aid the understanding of complex auditory scenes, even when
only a stereo mixdown is available in the audio domain, \eg identifying which
musicians are playing in large musical ensembles. In this paper, we consider a
vision-based approach to note onset detection. As a case study we focus on
challenging, real-world clarinetist videos and carry out preliminary
experiments on a 3D convolutional neural network based on multiple streams and
purposely avoiding temporal pooling. We release an audiovisual dataset with 4.5
hours of clarinetist videos together with cleaned annotations which include
about 36,000 onsets and the coordinates for a number of salient points and
regions of interest. By performing several training trials on our dataset, we
learned that the problem is challenging. We found that the CNN model is highly
sensitive to the optimization algorithm and hyper-parameters, and that treating
the problem as binary classification may prevent the joint optimization of
precision and recall. To encourage further research, we publicly share our
dataset, annotations and all models and detail which issues we came across
during our preliminary experiments.
