Diagnostics of the RIAA Equalizer in a Turntable Using Artificial Neural Network

Grzegorz Makarewicz, Piotr Bilski

Abstract

The following paper presents the methodology of RIAA equalizer condition analysis based on measurements of its amplitude and phase characteristics. The RIAA equalizer is used during the signal recording and is an integral part of modern turntables. It’s parameters determine the quality of the music being played. The task is to determine the critical values of electronic components (capacitors) based on the characteristics of signals observed at the circuit’s output. It is considered difficult due to the presence of noise, elements’ tolerances, and simultaneous drift of several system’s parameters. The presented methodology uses the Artificial Intelligence (AI) module that implements the task of parameter identification. The knowledge exploited by the AI-based module is extracted during machine learning, based on the dataset obtained during the simulations of the equalizer’s computer model. For the decisionmaking module, the standard tool for the regression tasks, i.e. RBF-type Artificial Neural Network (ANN) was used. The obtained results allow for considering the potentially high usefulness of the presented approach for the parameters identification in electronic circuits used in audio technology.

Keywords

RIAA correction, turntable, neural network, artificial intelligence, machine learning

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IMEKO-TC10-2020-026.pdf

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IMEKO TC

TC10 - Measurement for Diagnostics, Optimization and Control

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