@ARTICLE{Pourseiedrezaei_Mehdi_Prediction_2019,
 author={Pourseiedrezaei, Mehdi and Loghmani, Ali and Keshmiri, Mehdi},
 volume={vol. 44},
 number={No 3},
 journal={Archives of Acoustics},
 pages={561-573},
 howpublished={online},
 year={2019},
 publisher={Polish Academy of Sciences, Institute of Fundamental Technological Research, Committee on Acoustics},
 abstract={In this paper, a modified sound quality evaluation (SQE) model is developed based on combination of an optimized artificial neural network (ANN) and the wavelet packet transform (WPT). The presented SQE model is a signal processing technique, which can be implemented in current microphones for predicting the sound quality. The proposed method extracts objective psychoacoustic metrics including loudness, sharpness, roughness, and tonality from sound samples, by using a special selection of multi-level nodes of the WPT combined with a trained ANN. The model is optimized using the particle swarm optimization (PSO) and the back propagation (BP) algorithms. The obtained results reveal that the proposed model shows the lowest mean square error and the highest correlation with human perception while it has the lowest computational cost compared to those of the other models and software.},
 type={Article},
 title={Prediction of Psychoacoustic Metrics Using Combination of Wavelet Packet Transform and an Optimized Artificial Neural Network},
 URL={http://ochroma.man.poznan.pl/Content/112818/PDF/aoa.2019.129271.pdf},
 doi={10.24425/aoa.2019.129271},
 keywords={sound quality measurement, psychoacoustic metrics, wavelet packet transform, optimized artificial neural network},
}