ArticlePloS one2025
RETRACTED: Enhancing pipa tuning stability with piezoelectric materials: An adaptive system for real-time performance adjustment.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
Corrections and comments
- Retraction · 2026-01-29Author Unresponsive · Concerns/Issues about Article · Investigation by Journal/Publisher · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThe 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.
methodsA 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.
resultsThe 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.
conclusionThis 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.
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