AHSWN Home · Issue Contents · Forthcoming Papers

An Improved Wireless Sensor Network for Secure and Real-Time Violin Performance Assessment in the Domain of 6G-IoT
Xuan Xie

Currently, the music industry is the main focus of many researchers, scholars, and experts. However, there is limited research on real-time assessment and feedback mechanisms in the mentioned industry. Therefore, there is a pressing need for innovative solutions in the context of advancing musical education and performance that facilitate real-time assessment and feedback for musicians, particularly violinists. The conventional evaluation methods often lack the adaptability and immediacy required for effective skill enhancement. Keeping these in mind, this research paper proposes the Violin Real-Time Response to Data Collection Model (VRRTDCM) for real-time violin performance assessment within the framework of 6G Internet of Things (6G-IoT). In order to facilitate real-time performance for violinists, this research work presents a comprehensive system architecture that integrates a robust Wireless Sensor Network (WSN) by highlighting modular components for independent development and elevations. To capture critical performance metrics like bow speed, pressure, and angle, this research deploys low-power and lightweight sensors attached to the violin and its bow. The proposed framework uses WSNs and LSTM-CNN models to provide accurate evaluation while also ensuring secure, scalable, and reliable data transmission, making it an excellent choice for real-time performance assessment. Upon collecting data from the violin, the deployed sensors wirelessly transmit the same to a centralized Data Collection Hub utilizing secure communication protocols such as LoRa or Zigbee. By compiling the sensor data into a unified dataset that supports high temporal resolution for real-time analysis the centralized hub performs data aggregation and synchronization, which then passes to the Processing and Analysis Unit. This unit applies advanced algorithms, including Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN), to evaluate performance metrics and provide immediate feedback by enabling musicians to make real-time adjustments. To ensure secure storage of all processed data, the data is handed over to the Data Storage and Feedback Unit by providing a sequential log of performance metrics while enabling a real-time feedback interface that visualizes key metrics. Experimental evaluations of the proposed VRRTDCM are compared with existing models, showing that our proposed model has highly superior performance by achieving an accuracy of 81.69% and an AUC-ROC of 0.89. from these results, it is clear that our suggested approach allows musicians to improve their techniques more efficiently by developing a deeper connection to their skill and elevating the quality of musical performance in educational settings.

Keywords: Violin Performance Assessment, wireless sensor network, 6G-IoT, real-time feedback, machine learning