Journal of Engineering, Project, and Production Management, 2026, 16(6), 2026-91
Performance Optimization of Digital Music Multi-Instrument Recognition Based on an Improved DCNN Model and Bi-LSTM
1 Associate
Professor, College of Art, Hebei University of Economics and Business,
Shijiazhuang, 050062, China, E-mail:
junhuizhao1@outlook.com (corresponding author).
2 Postgraduate Student, Music
Performance, Hebei University of Economics and Business, Shijiazhuang,
050062, China, E-mail: xiaohangjia@heu.uu.me
3 Postgraduate Student, Vocal
Performance, Hebei University of Economics and Business, Shijiazhuang,
050062, China, E-mail: yuxuanzh31@heu.uu.me
Project Management
Received January 21, 2026; revised March 29, 2026; accepted September 6
Available online September 20, 2026
Abstract: To solve the problems of low accuracy and insufficient robustness in instrument recognition in multi-instrument scenarios of digital music, a parallel structure combining a standard Convolution Neural Network and a multi-scale Dilated Convolution Neural Network (CNN-DCNN) is designed to extract multi-scale spatial acoustic features from Mel spectrograms. The feature sequence extracted by CNN-DCNN is fed into a Bidirectional Long Short-Term Memory (Bi-LSTM) network for bidirectional context modeling, and a self-attention mechanism is introduced to dynamically weight temporal features, capturing the inherent long-range temporal dependencies of music signals. The findings demonstrate that the proposed fusion-improved model achieves optimal performance across multiple key metrics, with mean accuracy, macro-average F1 score, and mean precision reaching 88.92%, 87.11% and 88.34%, respectively, and significantly outperforming other comparative models. Ablation experiments further confirm the effectiveness of the parallel convolutional structure, temporal modeling, and attention mechanism. The proposed model, by deeply fusing multi-scale spatial features with bidirectional temporal contextual information, provides a high-performance solution for multi-instrument recognition in complex acoustic scenarios and offers valuable references for intelligent music analysis and retrieval.
Keywords: Multi-instrument recognition; deep learning; dilated convolutional neural network; bidirectional long short-term memory network; attention mechanism; mel spectrogram.
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Citation: Zhao, J., Jia, X., and Zhou, Y. (2026). Performance Optimization of Digital Music Multi-Instrument Recognition Based on an Improved DCNN Model and Bi-LSTM. Journal of Engineering, Project, and Production Management, 16(6), 2026-91.
