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For many music analysis problems, we need to know the presence
of instruments for each time frame in a multi-instrument
musical piece. However, such a frame-level instrument recognition
task remains difficult, mainly due to the lack of labeled
datasets. To address this issue, we present in this paper a
large-scale dataset that contains synthetic polyphonic music
with frame-level pitch and instrument labels. Moreover, we
propose a simple yet novel network architecture to jointly predict

Deep convolutional neural networks (CNNs) are nowadays achieving significant leaps in different pattern recognition tasks including action recognition. Current CNNs are increasingly deeper, data-hungrier and this makes their success tributary of the abundance of labeled training data. CNNs also rely on max/average pooling which reduces dimensionality of output layers and hence attenuates their sensitivity to the availability of labeled data.

In this paper, we introduce a variation of a state-of-the-art real-time tracker (CFNet), which adds to the original algorithm robustness to target loss without a significant computational overhead. The new method is based on the assumption that the feature map can be used to estimate the tracking confidence more accurately.