  Instrument recognition is a fundamental task in music information retrieval,
yet little has been done to predict the presence of instruments in
multi-instrument music for each time frame. This task is important for not only
automatic transcription but also many retrieval problems. In this paper, we use
the newly released MusicNet dataset to study this front, by building and
evaluating a convolutional neural network for making frame-level instrument
prediction. We consider it as a multi-label classification problem for each
frame and use frame-level annotations as the supervisory signal in training the
network. Moreover, we experiment with different ways to incorporate pitch
information to our model, with the premise that doing so informs the model the
notes that are active per frame, and also encourages the model to learn
relative rates of energy buildup in the harmonic partials of different
instruments. Experiments show salient performance improvement over baseline
methods. We also report an analysis probing how pitch information helps the
instrument prediction task. Code and experiment details can be found at
\url{https://biboamy.github.io/instrument-recognition/}.
