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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

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

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Autor(es):
Grijalva, Felipe [1] ; Martini, Luiz Cesar [1] ; Masiero, Bruno [1] ; Goldenstein, Siome [2]
Número total de Autores: 4
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
Número total de Afiliações: 2
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