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 this https URL .
Our work differentiates itself from the prior arts in two aspects. First, we focus on frame-level instrument recognition. Second, we explicitly employ the result of multi-pitch estimation @cite_3 @cite_40 as additional inputs to our CNN model, with a design that is motivated by the observation that instruments have different pitch range and have unique energy distributions in the partials @cite_23 .
@cite_40 @cite_23 @cite_3
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