Article: Extraction of beat features for piano teaching performance based on improved autoencoder Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.28 pp.1 - 16 Abstract: In the process of piano teaching, accurate extraction of beat features is crucial for assessing the performance level. For the purpose of coping with the issue of insufficient characteristic extraction in the current study, this article first accelerates the convergence speed based on the asymmetric convolutional optimisation autoencoder (ACAE). Then, we obtain the note frequency of the piano performance through the start point detection algorithm, use the relationship between notes and beats to subdivide the beat detection interval, and realise the division of beats through the measurement of confidence level. Finally, the channel attention module (CAM) is introduced into ACAE to complete the adaptive weighting of beat characteristics of every channel, so as to obtain the deep beat characteristics. The experimental outcome demonstrates that the offered approach has a feature classification accuracy of 96.38% and can effectively extract beat features. Inderscience Publishers - linking academia, business and industry through research

Title: Extraction of beat features for piano teaching performance based on improved autoencoder

Authors: Yating Yang

Addresses: School of Music, Communication University of China, Nanjing 210000, China

Abstract: In the process of piano teaching, accurate extraction of beat features is crucial for assessing the performance level. For the purpose of coping with the issue of insufficient characteristic extraction in the current study, this article first accelerates the convergence speed based on the asymmetric convolutional optimisation autoencoder (ACAE). Then, we obtain the note frequency of the piano performance through the start point detection algorithm, use the relationship between notes and beats to subdivide the beat detection interval, and realise the division of beats through the measurement of confidence level. Finally, the channel attention module (CAM) is introduced into ACAE to complete the adaptive weighting of beat characteristics of every channel, so as to obtain the deep beat characteristics. The experimental outcome demonstrates that the offered approach has a feature classification accuracy of 96.38% and can effectively extract beat features.

Keywords: piano teaching; feature extraction; autocoder; beat division; asymmetric convolution.

DOI: 10.1504/IJICT.2025.147710

International Journal of Information and Communication Technology, 2025 Vol.26 No.28, pp.1 - 16

Received: 20 May 2025
Accepted: 08 Jun 2025
Published online: 25 Jul 2025 *

Title: Extraction of beat features for piano teaching performance based on improved autoencoder

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