  Unlike melody extraction and other aspects of music transcription, research
on playing technique detection is still in its early stages. Compared to
existing work mostly focused on playing technique detection for individual
single notes, we propose a general end-to-end method based on Sound Event
Detection by FCN for musical instrument playing technique detection. In our
case, we choose Erhu, a well-known Chinese bowed-stringed instrument, to
experiment with our method. Because of the limitation of FCN, we present an
algorithm to detect on variable length audio. The effectiveness of the proposed
framework is tested on a new dataset, its categorization of techniques is
similar to our training dataset. The highest accuracy of our 3 experiments on
the new test set is 87.31%. Furthermore, we also evaluate the performance of
the proposed framework on 10 real-world studio music (produced by midi) and 7
real-world recording samples to address the ability of generalization on our
model.
