scholarly journals Low-order Spherical Harmonic HRTF Restoration using a Neural Network Approach

2020 ◽  
Author(s):  
Benjamin Tsui ◽  
William A. P. Smith ◽  
Gavin Kearney
Keyword(s):  
Neural Network ◽  
Spherical Harmonic ◽  
High Frequency ◽  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Network Approach ◽  
Spectral Difference ◽  
Neural Network Approach ◽  
Frequency Representation ◽  
Head Related Transfer Function

Spherical harmonic (SH) interpolation is a commonly used method to spatially up-sample sparse Head Related Transfer Function (HRTF) datasets to denser HRTF datasets. However, depending on the number of sparse HRTF measurements and SH order, this process can introduce distortions in high frequency representation of the HRTFs. This paper investigates whether it is possible to restore some of the distorted high frequency HRTF components using machine learning algorithms. A combination of Convolutional Auto-Encoder (CAE) and Denoising Auto-Encoder (DAE) models is proposed to restore the high frequency distortion in SH interpolated HRTFs. Results are evaluated using both Perceptual Spectral Difference (PSD) and localisation prediction models, both of which demonstrate significant improvement after the restoration process.

scholarly journals Low-Order Spherical Harmonic HRTF Restoration Using a Neural Network Approach

2020 ◽  
Vol 10 (17) ◽  
pp. 5764
Author(s):  
Benjamin Tsui ◽  
William A. P. Smith ◽  
Gavin Kearney
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Spherical Harmonic ◽  
High Frequency ◽  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Network Approach ◽  
Spectral Difference ◽  
Neural Network Approach ◽  
Head Related Transfer Function

Spherical harmonic (SH) interpolation is a commonly used method to spatially up-sample sparse head related transfer function (HRTF) datasets to denser HRTF datasets. However, depending on the number of sparse HRTF measurements and SH order, this process can introduce distortions into high frequency representations of the HRTFs. This paper investigates whether it is possible to restore some of the distorted high frequency HRTF components using machine learning algorithms. A combination of convolutional auto-encoder (CAE) and denoising auto-encoder (DAE) models is proposed to restore the high frequency distortion in SH-interpolated HRTFs. Results were evaluated using both perceptual spectral difference (PSD) and localisation prediction models, both of which demonstrated significant improvement after the restoration process.

A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area

2020 ◽  
Vol 701 ◽  
pp. 134413 ◽  
Author(s):  
Dieu Tien Bui ◽  
Nhat-Duc Hoang ◽  
Francisco Martínez-Álvarez ◽  
Phuong-Thao Thi Ngo ◽  
Pham Viet Hoa ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
High Frequency ◽  
Flash Flood ◽  
Tropical Storm ◽  
Network Approach ◽  
Neural Network Approach ◽  
Flood Susceptibility ◽  
Deep Learning Neural Network

The psychopharmacology of latent inhibition: A neural network approach

1997 ◽  
Author(s):  
Nestor A. Schmajuk ◽  
Catalin V. Buhusi ◽  
Jeffrey A. Gray
Keyword(s):  
Neural Network ◽  
Latent Inhibition ◽  
Network Approach ◽  
Neural Network Approach

A fuzzy neural network approach for modeling the growth kinetics of FeB and Fe2B layers during the boronizing process

2018 ◽  
Vol 106 (6) ◽  
pp. 603 ◽  
Author(s):  
Bendaoud Mebarek ◽  
Mourad Keddam
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Fuzzy Neural Network ◽  
Treatment Time ◽  
Calculation Model ◽  
Average Error ◽  
Network Approach ◽  
Neural Network Approach ◽  
Fuzzy Neural ◽  
Kinetics Of

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.

A neural network approach to anomaly detection in spectra

1997 ◽  
Author(s):  
Daniel Benzing ◽  
Kevin Whitaker ◽  
Dedra Moore ◽  
Daniel Benzing ◽  
Kevin Whitaker ◽  
...  
Keyword(s):  
Neural Network ◽  
Anomaly Detection ◽  
Network Approach ◽  
Neural Network Approach
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