Digital inheritance and innovative development of music based on deep learning and the Internet of Things

The preservation and innovative development of traditional music are essential for sustaining cultural heritage, artistic identity, and creative expression. Rapid digitalization and a lack of systematic preservation threaten the inheritance of invaluable musical resources. This research proposes a framework for digital inheritance and innovative music development by integrating deep learning (DL) with the Internet of Things (IoT). IoT-enabled sensors were used to capture real-time performances of vocal, instrumental, and dramatic music, recording acoustic, temporal, and environmental parameters. The collected signals underwent preprocessing, including noise reduction, normalization, and Short-Time Fourier Transform (STFT), to ensure accurate feature representation of musical patterns. For pattern recognition and creative innovation, an Intelligent Water Drop-driven Versatile Deep Neural Network (IWD-VDNN) was employed. The VDNN is the core DL model designed to analyze, preserve, and generate traditional music patterns. The IWD algorithm optimized the learning process, improving convergence and model efficiency, enabling the system to generate new musical sequences inspired by traditional styles. The proposed method implemented in Python significantly enhanced digital music inheritance and innovation. It achieved 94% accuracy, 92.2% recall, a Style Match Accuracy of 95%, and a Musical Quality Score of 4.91, demonstrating superior performance in preserving traditional music while enabling interactive, creative, and high-quality music generation, effectively bridging cultural heritage with innovative composition and adaptive digital learning. Integration of IoT and DL provides a scalable, accurate, and interactive platform for safeguarding traditional music while promoting its creative evolution for future generations.