As a guest user you are not logged in or recognized by your IP address. You have
access to the Front Matter, Abstracts, Author Index, Subject Index and the full
text of Open Access publications.
Now head-related transfer function (HRTF) databases we already have are not spatially continuous, it's necessary to reconstruct a high spatial resolution HRTF database to solve this problem. Based on the minimum-phase HRIRs (Head-related Impulse Responses), we analyze and compare the errors of three traditional methods include linear, cubic, and spline interpolation. Furthermore, we propose a method using RBF (radial basis function) artificial neural network to interpolate the minimum-phase HRIRs. The minimum-phase reconstructed empirical HRIRs are used to train and establish the neural network. By training RBF neural network, we can approximate HRIRs in any spatial positions. The experimental results show that this method retains the advantages of minimum phase HRIR. It has the minimum group delay, minimum filter length, less interpolation error and a good performance for estimation. The proposed method makes more convenient to obtain HRTFs at any required spatial positions in synthesizing virtual 3D sound.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.