# Playing Technique Detection by Fusing Note Onset Information in Guzheng Performance

arXiv: https://arxiv.org/abs/2209.08774 · HTML: https://mlsanity.com/paper/2209.08774/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 19 Sep 2022.

## Builds on (most cited first, 5 of 5)

- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Onsets and Frames: Dual-Objective Piano Transcription](https://mlsanity.com/paper/1710.11153/) (2017)
- [High-resolution Piano Transcription with Pedals by Regressing Onset and Offset Times](https://mlsanity.com/paper/2010.01815/) (2020)
- [Extended playing techniques: The next milestone in musical instrument recognition](https://mlsanity.com/paper/1808.09730/) (2018)
- [Musical Instrument Playing Technique Detection Based on FCN: Using Chinese Bowed-Stringed Instrument as an Example](https://mlsanity.com/paper/1910.09021/) (2019)

## Built on by (most cited first, 4 of 4)

- [Frame-Level Multi-Label Playing Technique Detection Using Multi-Scale Network and Self-Attention Mechanism](https://mlsanity.com/paper/2303.13272/) (2023)
- [MERTech: Instrument Playing Technique Detection Using Self-Supervised Pretrained Model With Multi-Task Finetuning](https://mlsanity.com/paper/2310.09853/) (2023)
- [PESTO: Real-Time Pitch Estimation with Self-supervised Transposition-equivariant Objective](https://mlsanity.com/paper/2508.01488/) (2025)
- [VioPTT: Violin Technique-Aware Transcription from Synthetic Data Augmentation](https://mlsanity.com/paper/2509.23759/) (2025)

## Abstract (arXiv)

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.