Improvement of accuracy in a sound synthesis method using Evolutionary Product Unit Networks

Auralization through binaural transfer path analysis and synthesis is a useful tool to analyze how contributions from different sources affect the perception of sound. This paper presents a novel model based on the auralization of sound sources through the study of the behavior of the system with respect to frequency. The proposed approach is a combined model using the airborne source quantification (ASQ) technique for low-mid frequencies (⩽2.5 kHz) and Evolutionary Product-Unit Neural Networks (EPUNNs) for high frequencies (>2.5 kHz), which improve overall accuracy. The accuracy of all models has been evaluated in terms of the Mean Squared Error (MSE) and the Standard Error of Prediction (SEP), the combined model obtaining the smallest value for high frequencies. Moreover, the best prediction model was established based on sound quality metrics, the proposed method showing better accuracy than the ASQ technique at high frequencies in terms of loudness, sharpness and 1/3rd octave bands.

► We model sound sources by means of monopole sound source quantification technique.
► We propose a model based on Artificial Neural Network for high frequencies.
► The novel model improves the accuracy for high frequencies based on loudness metrics.