ITD Modeling Based on Anthropometrics and KEMAR Coefficients Using Deep Neural Networks ITD and ILD, versus source arrival direction, serve as essential binaural cues for spatial hearing. Individualized ITD and ILD can be used to render better 3D audio than a nonindividualized one. Due to the correlation between ITD and some anthropometric features, machine learning, such as principal component analysis (PCA) and deep neural networks (DNNs), have become important methods to deploy individualized ITDs. The available measured ITDs do not match the exact sound source directions. An ITD correction method will be presented to overcome the irregularities that occurr due to subject head movements during database creation measurements. KEMARxe2x80x99s ITD coefficients are utilized to correct the misplacement of a subjectxe2x80x99s ITD. DNNs are used to obtain a new subjectxe2x80x99s ITD for 1250 different azimuth and elevation angles. Mean absolute error (MAE) is used to compare the proposed ITD model with the available analytical models.

Optimization of Headrelated Transfer Function (HRTF) Models This paper presents a deeplearning technique for modeling Headrelated Transfer Function (HRTF) using a large dataset from IRCAM 7 of 49 subjects with 115 directionssubject. We use a logspectral distortion metric that we have used to optimize the performance of the deeplearning model. The presented model first uses a sparse autoencoder (AE) for capturing the salient representation (peaks, and notches for elevation) from the usercaptured HRTFs, in its latent representation, and then uses a generalized regression neural network (GRNN) to approximate the latent representation of the corresponding HRTFs at arbitrary angles. The sparse AE then decodes the GRNN output to reconstruct the HRTF. The proposed approach is shown to outperform, objectively using the logspectral distortion, the stateoftheart linearleastsquares optimum solution (viz., derived from Principal Component Analysis) to perform better generalization over the HRTFs in the IRCAM dataset. Towards this, Bayesian optimization has been performed to tune the hyperparameters of the sparse AE and the GRNN.