Siamese Residual Neural Network for Musical Shape Evaluation in Piano Performance Assessment

Xiaoquan Li, Stephan Weiss, Yijun Yan, Yinhe Li, Jinchang Ren, John Soraghan, Ming Gong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Downloads (Pure)

Abstract

Understanding and identifying musical shape plays an important role in music education and performance assessment. To simplify the otherwise time- and cost-intensive musical shape evaluation, in this paper we explore how artificial intelligence (AI) driven models can be applied. Considering musical shape evaluation as a classification problem, a light-weight Siamese residual neural network (S-ResNN) is proposed to automatically identify musical shapes. To assess the proposed approach in the context of piano musical shape evaluation, we have generated a new dataset, namely MSED-4k, containing 4116 music pieces derived by 147 piano preparatory exercises and performed in 28 categories of musical shapes. The experimental results show that the S-ResNN significantly outperforms a number of benchmark methods in terms of the precision, recall and F1 score.

Original languageEnglish
Title of host publication3st European Signal Processing Conference, EUSIPCO 2023 - Proceedings
PublisherIEEE
Pages216-220
Number of pages5
ISBN (Electronic)9789464593600
ISBN (Print)9798350328110
DOIs
Publication statusPublished - 1 Nov 2023
Event31st European Signal Processing Conference, EUSIPCO 2023 - Helsinki, Finland
Duration: 4 Sept 20238 Sept 2023

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491
ISSN (Electronic)2076-1465

Conference

Conference31st European Signal Processing Conference, EUSIPCO 2023
Country/TerritoryFinland
CityHelsinki
Period4/09/238/09/23

Keywords

  • Audio Classification
  • Musical Shape Evaluation
  • Piano Performance Assessment
  • Siamese Network

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Siamese Residual Neural Network for Musical Shape Evaluation in Piano Performance Assessment'. Together they form a unique fingerprint.

Cite this