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.

本文提出了一个利用全卷积神经网络的端到端古筝演奏技巧检测系统，通过训练独立的起始检测器和帧级演奏技巧检测器以实现对可变长度音频的检测，这可以应用于各种音乐厂牌。同时，作者还从多个音乐厂牌和实际演出中创建了一个新的数据集 GZ_IsoTech，实现了 87.97％的帧级准确性和 80.76％的音符级 F1 分数，并且远远超过现有工作的表现，证明了该方法在演奏技巧检测中的有效性。