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

Vision-based Detection of Acoustic Timed Events: a Case Study on Clarinet Note Onsets