A Recommender System for Improving Median Plane Sound Localization Performance Based on a Nonlinear Representation of HRTFs

Autor(es)

Grijalva, Felipe ^[1] ; Martini, Luiz Cesar ^[1] ; Masiero, Bruno ^[1] ; Goldenstein, Siome ^[2]

Afiliação do(s) autor(es)

^[1] Univ Estadual Campinas, Sch Elect & Comp Engn, BR-13083852 Campinas, SP - Brazil

^[2] Univ Estadual Campinas, Inst Comp, BR-13083970 Campinas, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: IEEE ACCESS; v. 6, p. 24829-24836, 2018.
• Citações Web of Science: 0

Resumo

We propose a new method to improve median plane sound localization performance using a nonlinear representation of head-related transfer functions (HRTFs) and a recommender system. First, we reduce the dimensionality of an HRTF data set with multiple subjects using manifold learning in conjunction with a customized intersubject graph which takes into account relevant prior knowledge of HRTFs. Then, we use a sound localization model to estimate a subject's localization performance in terms of polar error and quadrant error rate. These metrics are merged to form a single rating per HRTF pair that we feed into a recommender system. Finally, the recommender system takes the low-dimensional HRTF representation as well as the ratings obtained from the localization model to predict the best HRTF set, possibly constructed by mixing HRTFs from different individuals, that minimizes a subject's localization error. The simulation results show that our method is capable of choosing a set of HRTFs that improves the median plane localization performance with respect to the mean localization performance using non-individualized HRTFs. Moreover, the localization performance achieved by our HRTF recommender system shows no significant difference to the localization performance observed with the best matching non-individualized HRTFs but with the advantage of not having to perform listening tests with all individuals' HRTFs from the database. (AU)

• Processo FAPESP: 12/50468-6 - Visão para o cego: traduzindo conceitos visuais 3D em informações de 3D de áudio
• Beneficiário: Siome Klein Goldenstein
• Modalidade de apoio: Auxílio à Pesquisa - Parceria para Inovação Tecnológica - PITE
• Processo FAPESP: 14/14630-9 - Aprendizado de máquina em processamento de sinais aplicado ao áudio espacial
• Beneficiário: Felipe Leonel Grijalva Arévalo
• Modalidade de apoio: Bolsas no Brasil - Doutorado
• Processo FAPESP: 13/21349-1 - Visão para o cego: traduzindo conceitos visuais 3D em informações de 3D de áudio
• Beneficiário: Felipe Leonel Grijalva Arévalo
• Modalidade de apoio: Bolsas no Brasil - Mestrado