- Tytuł:
- Head-Related Transfer Function Selection Using Neural Networks
- Autorzy:
-
Yao, S.-N.
Collins, T.
Liang, C. - Powiązania:
- https://bibliotekanauki.pl/articles/176307.pdf
- Data publikacji:
- 2017
- Wydawca:
- Polska Akademia Nauk. Czytelnia Czasopism PAN
- Tematy:
-
head-related transfer function
neural networks
localisation
music
audio
anthropometry
pinna - Opis:
- In binaural audio systems, for an optimal virtual acoustic space a set of head-related transfer functions (HRTFs) should be used that closely matches the listener’s ones. This study aims to select the most appropriate HRTF dataset from a large database for users without the need for extensive listening tests. Currently, there is no way to reliably reduce the number of datasets to a smaller, more manageable number without risking discarding potentially good matches. A neural network that estimates the appropriateness of HRTF datasets based on input vectors of anthropometric measurements is proposed. The shapes and sizes of listeners’ heads and pinnas were measured using digital photography; the measured anthropometric parameters form the feature vectors used by the neural network. A graphical user interface (GUI) was developed for participants to listen to music transformed using different HRTFs and to evaluate the fitness of each HRTF dataset. The listening scores recorded were the target outputs used to train the neural networks. The aim was to learn a mapping between anthropometric parameters and listener’s perception scores. Experimental validations were performed on 30 subjects. It is demonstrated that the proposed system produces a much more reliable HRTF selection than previously used methods.
- Źródło:
-
Archives of Acoustics; 2017, 42, 3; 365-373
0137-5075 - Pojawia się w:
- Archives of Acoustics
- Dostawca treści:
- Biblioteka Nauki
Artykuł