Title: Music singing emotion classification and recognition method based on deep representation learning

Authors: Li Pan; Jiayao Liang; Yuyang Chen

Addresses: School of Music and Dance, Mudanjiang Normal University, Mudanjiang, 157011, China ' School of Music and Dance, Mudanjiang Normal University, Mudanjiang, 157011, China ' Academy Milan, Milan, 20149, Italy

Abstract: This paper proposes a music performance emotion classification and recognition method based on deep representation learning to improve accuracy. Firstly, an emotional vocabulary system is constructed, and feature parameters such as range, intensity, and rhythm are extracted based on mathematical modelling. Secondly, a multi-layer neural network model is constructed through deep representation learning to select the acoustic features with the highest correlation with emotional terms for encoding and dimensionality reduction. A deep learning architecture is designed that includes a dynamic input layer, embedding layer, convolutional layer, and maximum sentiment term pooling layer. Finally, unsupervised pre training and feature concatenation strategies are adopted to automatically mine and classify complex emotional patterns in music singing. The experimental results show that the proposed method achieves an accuracy of 90% to 98% in extracting emotional features of music singing, and a classification recognition accuracy of 97.32% to 99.74%.

Keywords: deep representation learning; music singing; emotion classification recognition; acoustic feature encoding.

DOI: 10.1504/IJBM.2026.156235

International Journal of Biometrics, 2026 Vol.18 No.5, pp.501 - 515

Received: 23 Apr 2025
Accepted: 21 Jul 2025
Published online: 08 Sep 2026 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article