Intro
The Erhu Performance Technique Recognition Model is an audio analysis tool based on deep learning techniques, aiming to automatically distinguish different techniques in erhu performance. By deeply analyzing the acoustic characteristics of erhu music, the model is able to recognize 11 basic playing techniques, including split bow, pad bow, overtone, continuous bow, glissando, big glissando, strike bow, pizzicato, throw bow, staccato bow, vibrato, tremolo and vibrato. Through time-frequency conversion, feature extraction and pattern recognition, the model can accurately categorize the complex techniques of erhu performance, which provides an efficient technical support for music information retrieval, music education, and research on the art of erhu performance. The application of this model not only enriches the research in the field of music acoustics, but also opens up a new way for the inheritance and innovation of traditional music.
Demo
https://huggingface.co/spaces/ccmusic-database/erhu-playing-tech
Usage
from modelscope import snapshot_download
model_dir = snapshot_download("ccmusic-database/erhu_playing_tech")
Maintenance
git clone [email protected]:ccmusic-database/erhu_playing_tech
cd erhu_playing_tech
Results
| Backbone | Mel | CQT | Chroma |
|---|---|---|---|
| Swin-S | 0.978 | 0.940 | 0.903 |
| Swin-T | 0.994 | 0.958 | 0.957 |
| AlexNet | 0.960 | 0.970 | 0.933 |
| ConvNeXt-T | 0.994 | 0.993 | 0.954 |
| ShuffleNet-V2-X2.0 | 0.990 | 0.923 | 0.887 |
| GoogleNet | 0.986 | 0.981 | 0.908 |
| SqueezeNet1.1 | 0.932 | 0.939 | 0.875 |
| Average | 0.976 | 0.958 | 0.917 |
Best Result
A demo result of Swin-T fine-tuning by mel:
| Loss curve |  |
|---|---|
| Training and validation accuracy |  |
| Confusion matrix |  |
Dataset
https://huggingface.co/datasets/ccmusic-database/erhu_playing_tech
Mirror
https://www.modelscope.cn/models/ccmusic-database/erhu_playing_tech
Evaluation
https://github.com/monetjoe/ccmusic_eval
Cite
@dataset{zhaorui_liu_2021_5676893,
author = {Monan Zhou, Shenyang Xu, Zhaorui Liu, Zhaowen Wang, Feng Yu, Wei Li and Baoqiang Han},
title = {CCMusic: an Open and Diverse Database for Chinese Music Information Retrieval Research},
month = {mar},
year = {2024},
publisher = {HuggingFace},
version = {1.2},
url = {https://huggingface.co/ccmusic-database}
}