AES Convention Papers Forum

Dataset Augmentation and Dimensionality Reduction of Pinna-Related Transfer Functions

Document Thumbnail

Ef?cient modeling of the inter-individual variations of head-related transfer functions (HRTF) is a key matter to the individualization of binaural synthesis. In previous work, we augmented a dataset of 119 pairs of ear shapes and pinna-related transfer functions (PRTFs), thus creating a wide dataset of 1005 ear shapes and PRTFs generated by random ear drawings (WiDESPREaD) and acoustical simulations. In this article, we investigate the dimensionality reduction capacity of two principal component analysis (PCA) models of magnitude PRTFs, trained on WiDESPREaD and on the original dataset, respectively. We ?nd that the model trained on the WiDESPREaD dataset performs best, regardless of the number of retained principal components.

AES Convention: Paper Number:
Publication Date:

Click to purchase paper as a non-member or you can login as an AES member to see more options.

No AES members have commented on this paper yet.

Subscribe to this discussion

RSS Feed To be notified of new comments on this paper you can subscribe to this RSS feed. Forum users should login to see additional options.

Start a discussion!

If you would like to start a discussion about this paper and are an AES member then you can login here:

If you are not yet an AES member and have something important to say about this paper then we urge you to join the AES today and make your voice heard. You can join online today by clicking here.

AES - Audio Engineering Society