Enhancing pipa tuning stability with piezoelectric materials: An adaptive system for real-time performance adjustment

Abstract

Introduction The study explores the development and performance evaluation of a Neuro-Fuzzy Inference System (NFIS) for adaptive tuning of a pipa string instrument under varying environmental conditions. The NFIS adjusts string tension in real-time based on voltage, temperature, and humidity sensor inputs by integrating piezoelectric sensors with IoT capabilities. The primary objective is to maintain tuning accuracy within ±0.1 Hz, even with environmental fluctuations, thus improving the stability and consistency of musical performance. Methods A dataset of 2,000 samples was collected, including voltage (0.1–5 V), temperature (10–40°C), and humidity (20–90% RH) values, along with corresponding output adjustments. The NFIS utilised Gaussian membership functions to categorise sensor inputs into linguistic terms (e.g., “High Voltage,” “Medium Temperature”), and a comprehensive rule base of 40 rules was established for adaptive tuning. Training of the NFIS was conducted using gradient-descent backpropagation with a learning rate of 0.01 and L2 regularisation, validated through 5-fold cross-validation. Real-time performance data was transmitted via an ESP32 microcontroller to an AWS IoT Core database, with user adjustments and data visualisation provided through a mobile application. Results The NFIS was highly well tuned with a mean pitch deviation of only ±0.08 Hz at stable and varying environmental conditions. We have cross-validated the model, and it produced an average MSE of 0.012 across folds, which speaks to the robustness of the model. During an 8-hour test period, our IoT system achieved an average data transmission latency of 120 ms on the server and 99.8% system uptime, with our error correction mechanisms ensuring 98% data integrity. The compensated voltage deviated less than ±0.1 V from the baseline voltage at varying temperatures and humidity, and the environmental compensation minimised the voltage deviations within the original compensation limits. Conclusion This NFIS-based adaptive Tuning System keeps the tuning accurate during environmental changes. In conjunction with IoT for real-time monitoring and adaptive learning capabilities, this technology adds more responsiveness and reliability, thus making it an efficient tool for musicians to perform consistently.