  The Guzheng is a kind of traditional Chinese instruments with diverse playing
techniques. Instrument playing techniques (IPT) play an important role in
musical performance. However, most of the existing works for IPT detection show
low efficiency for variable-length audio and provide no assurance in the
generalization as they rely on a single sound bank for training and testing. In
this study, we propose an end-to-end Guzheng playing technique detection system
using Fully Convolutional Networks that can be applied to variable-length
audio. Because each Guzheng playing technique is applied to a note, a dedicated
onset detector is trained to divide an audio into several notes and its
predictions are fused with frame-wise IPT predictions. During fusion, we add
the IPT predictions frame by frame inside each note and get the IPT with the
highest probability within each note as the final output of that note. We
create a new dataset named GZ_IsoTech from multiple sound banks and real-world
recordings for Guzheng performance analysis. Our approach achieves 87.97% in
frame-level accuracy and 80.76% in note-level F1-score, outperforming existing
works by a large margin, which indicates the effectiveness of our proposed
method in IPT detection.
