Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Evolutionary product-unit neural networks classifiers
Neurocomputing
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Prediction of diesel engine performance using biofuels with artificial neural network
Expert Systems with Applications: An International Journal
A two-stage algorithm in evolutionary product unit neural networks for classification
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Evolutional RBFNs prediction systems generation in the applications of financial time series data
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Evolutionary Computation: A New Transactions
IEEE Transactions on Evolutionary Computation
Hybridization of evolutionary algorithms and local search by means of a clustering method
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A general regression neural network
IEEE Transactions on Neural Networks
An evolutionary algorithm that constructs recurrent neural networks
IEEE Transactions on Neural Networks
Hi-index | 12.05 |
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 (=2.5kHz), 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.