author = "Grijalva, Felipe and Goldenstein, Siome and Florencio, Dinei and 
                         Martini, Luiz",
          affiliation = "School of Electrical and Computer Engineering, University of 
                         Campinas, Campinas, Brazil. and Institute of Computing, University 
                         of Campinas, Campinas, Brazil. and Multimedia, Interaction and 
                         Communication Group, Microsoft Research, Redmond, WA, USA. and 
                         School of Electrical and Computer Engineering, University of 
                         Campinas, Campinas, Brazil.",
                title = "Manifold learning using Isomap applied to spatial audio 
            booktitle = "Proceedings...",
                 year = "2015",
               editor = "Segundo, Maur{\'{\i}}cio Pamplona and Faria, Fabio Augusto",
         organization = "Conference on Graphics, Patterns and Images, 28. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "Isomap, Manifold Learning, Spatial Audio.",
             abstract = "As augmented reality applications become more important, there is 
                         increasing effort in spatial audio research. The term spatial 
                         audio or 3D sound refers to techniques where a person's anatomy is 
                         modeled as digital filters. By filtering a sound source with these 
                         filters, a listener is capable of perceiving a sound as though it 
                         were reproduced at a specific spatial location. In the frequency 
                         domain, these filters are known as Head-Related Transfer 
                         Functions(HRTFs). A significant problem for the implementation of 
                         3D sound systems is the fact that spectral features of HRTFs 
                         differ widely among individuals due to their anatomical 
                         differences. Thus, it is necessary to personalize them to 
                         guarantee high quality sound perception. With this aim, we 
                         introduce a new anthropometric-based method for customizing of 
                         HRTFs in the horizontal plane using manifold learning. The method 
                         uses Isomap, artificial neural networks (ANN), and a 
                         neighborhood-based reconstruction procedure. We first modify 
                         Isomap's graph construction step to emphasize the individuality of 
                         HRTFs and perform a customized nonlinear dimensionality reduction 
                         of the HTRFs. We then use an ANN to model the nonlinear 
                         relationship between anthropometric features and our 
                         low-dimensional HRTFs. Finally, we use a neighborhood-based 
                         reconstruction approach to reconstruct the HRTF from the estimated 
                         low-dimensional version. Simulations show that our approach 
                         performs better than PCA (Principal Component Analysis) and 
                         confirm that Isomap is capable of discovering the underlying 
                         nonlinear relationships of sound perception.",
  conference-location = "Salvador",
      conference-year = "Aug. 26-29, 2015",
             language = "en",
                  ibi = "8JMKD3MGPBW34M/3JUMPP2",
                  url = "",
           targetfile = "WTD sibgrapi 2015 Camera Ready.pdf",
        urlaccessdate = "2021, Dec. 04"