Abstract
The purpose of this paper is to explore the reform and innovation mode of music teaching driven by artificial intelligence (AI). By introducing the Dilated Convolutional Neural Network (DCNN) algorithm and further integrating the attention mechanism, an audio recognition model based on a Multi-Branch Fusion Network Based on Dilated Convolution and Attention Mechanism (MBFN-DCAM) is constructed to improve the accuracy and efficiency of music audio recognition. Horizontal comparison experiments show that MBFN-DCAM outperforms 6 representative state-of-the-art (SOTA) models, including Audio Spectrogram Transformer (AST) and ResNet-50, with a recognition accuracy of 95.65% ± 0.35% (p < 0.01). Validated by a randomized controlled trial (n = 60), feedback provided by the model significantly improves students’ pitch accuracy and enhances their Music Self-Efficacy Scale (MES). Furthermore, the inference latency of the model on Jetson Nano is only 82.4 ms, fully demonstrating its deployment advantages in resource-constrained environments. This paper provides an efficient, robust, and educationally effective technical approach for intelligent music teaching evaluation.
Data availability
The datasets used and/or analysed during the current study available from the corresponding author Ningning Shi on reasonable request via e-mail [2003248@hlju.edu.cn](mailto:2003248@hlju.edu.cn) .
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Acknowledgments
This work was supported by Research Project on the Protection and Contemporary Value of Heilongjiang Manchu Music, 2025 Basic Research Business Expenses of Provincial Universities in Heilongjiang Province.
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Chang Liu: Conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing—original draft preparation. Ningning Shi: methodology, software, validation, formal analysis. Shen Jiang: writing—review and editing, visualization, supervision, project administration, funding acquisition.
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The studies involving human participants were reviewed and approved by College of Arts, Heilongjiang University Ethics Committee (Approval Number: 2021.398472). The participants provided their written informed consent to participate in this study. All methods were performed in accordance with relevant guidelines and regulations.
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Liu, C., Shi, N. & Jiang, S. Artificial intelligence technology for music teaching reform mode under DCNN algorithm. Sci Rep (2026). https://doi.org/10.1038/s41598-026-45027-w
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DOI: https://doi.org/10.1038/s41598-026-45027-w
