5,327 research outputs found

    Aprendizado de variedades para a síntese de áudio espacial

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    Orientadores: Luiz César Martini, Bruno Sanches MasieroTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: O objetivo do áudio espacial gerado com a técnica binaural é simular uma fonte sonora em localizações espaciais arbitrarias através das Funções de Transferência Relativas à Cabeça (HRTFs) ou também chamadas de Funções de Transferência Anatômicas. As HRTFs modelam a interação entre uma fonte sonora e a antropometria de uma pessoa (e.g., cabeça, torso e orelhas). Se filtrarmos uma fonte de áudio através de um par de HRTFs (uma para cada orelha), o som virtual resultante parece originar-se de uma localização espacial específica. Inspirados em nossos resultados bem sucedidos construindo uma aplicação prática de reconhecimento facial voltada para pessoas com deficiência visual que usa uma interface de usuário baseada em áudio espacial, neste trabalho aprofundamos nossa pesquisa para abordar vários aspectos científicos do áudio espacial. Neste contexto, esta tese analisa como incorporar conhecimentos prévios do áudio espacial usando uma nova representação não-linear das HRTFs baseada no aprendizado de variedades para enfrentar vários desafios de amplo interesse na comunidade do áudio espacial, como a personalização de HRTFs, a interpolação de HRTFs e a melhoria da localização de fontes sonoras. O uso do aprendizado de variedades para áudio espacial baseia-se no pressuposto de que os dados (i.e., as HRTFs) situam-se em uma variedade de baixa dimensão. Esta suposição também tem sido de grande interesse entre pesquisadores em neurociência computacional, que argumentam que as variedades são cruciais para entender as relações não lineares subjacentes à percepção no cérebro. Para todas as nossas contribuições usando o aprendizado de variedades, a construção de uma única variedade entre os sujeitos através de um grafo Inter-sujeito (Inter-subject graph, ISG) revelou-se como uma poderosa representação das HRTFs capaz de incorporar conhecimento prévio destas e capturar seus fatores subjacentes. Além disso, a vantagem de construir uma única variedade usando o nosso ISG e o uso de informações de outros indivíduos para melhorar o desempenho geral das técnicas aqui propostas. Os resultados mostram que nossas técnicas baseadas no ISG superam outros métodos lineares e não-lineares nos desafios de áudio espacial abordados por esta teseAbstract: The objective of binaurally rendered spatial audio is to simulate a sound source in arbitrary spatial locations through the Head-Related Transfer Functions (HRTFs). HRTFs model the direction-dependent influence of ears, head, and torso on the incident sound field. When an audio source is filtered through a pair of HRTFs (one for each ear), a listener is capable of perceiving a sound as though it were reproduced at a specific location in space. Inspired by our successful results building a practical face recognition application aimed at visually impaired people that uses a spatial audio user interface, in this work we have deepened our research to address several scientific aspects of spatial audio. In this context, this thesis explores the incorporation of spatial audio prior knowledge using a novel nonlinear HRTF representation based on manifold learning, which tackles three major challenges of broad interest among the spatial audio community: HRTF personalization, HRTF interpolation, and human sound localization improvement. Exploring manifold learning for spatial audio is based on the assumption that the data (i.e. the HRTFs) lies on a low-dimensional manifold. This assumption has also been of interest among researchers in computational neuroscience, who argue that manifolds are crucial for understanding the underlying nonlinear relationships of perception in the brain. For all of our contributions using manifold learning, the construction of a single manifold across subjects through an Inter-subject Graph (ISG) has proven to lead to a powerful HRTF representation capable of incorporating prior knowledge of HRTFs and capturing the underlying factors of spatial hearing. Moreover, the use of our ISG to construct a single manifold offers the advantage of employing information from other individuals to improve the overall performance of the techniques herein proposed. The results show that our ISG-based techniques outperform other linear and nonlinear methods in tackling the spatial audio challenges addressed by this thesisDoutoradoEngenharia de ComputaçãoDoutor em Engenharia Elétrica2014/14630-9FAPESPCAPE

    Semantic Image Synthesis via Adversarial Learning

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    In this paper, we propose a way of synthesizing realistic images directly with natural language description, which has many useful applications, e.g. intelligent image manipulation. We attempt to accomplish such synthesis: given a source image and a target text description, our model synthesizes images to meet two requirements: 1) being realistic while matching the target text description; 2) maintaining other image features that are irrelevant to the text description. The model should be able to disentangle the semantic information from the two modalities (image and text), and generate new images from the combined semantics. To achieve this, we proposed an end-to-end neural architecture that leverages adversarial learning to automatically learn implicit loss functions, which are optimized to fulfill the aforementioned two requirements. We have evaluated our model by conducting experiments on Caltech-200 bird dataset and Oxford-102 flower dataset, and have demonstrated that our model is capable of synthesizing realistic images that match the given descriptions, while still maintain other features of original images.Comment: Accepted to ICCV 201

    Acoustic Space Learning for Sound Source Separation and Localization on Binaural Manifolds

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    In this paper we address the problems of modeling the acoustic space generated by a full-spectrum sound source and of using the learned model for the localization and separation of multiple sources that simultaneously emit sparse-spectrum sounds. We lay theoretical and methodological grounds in order to introduce the binaural manifold paradigm. We perform an in-depth study of the latent low-dimensional structure of the high-dimensional interaural spectral data, based on a corpus recorded with a human-like audiomotor robot head. A non-linear dimensionality reduction technique is used to show that these data lie on a two-dimensional (2D) smooth manifold parameterized by the motor states of the listener, or equivalently, the sound source directions. We propose a probabilistic piecewise affine mapping model (PPAM) specifically designed to deal with high-dimensional data exhibiting an intrinsic piecewise linear structure. We derive a closed-form expectation-maximization (EM) procedure for estimating the model parameters, followed by Bayes inversion for obtaining the full posterior density function of a sound source direction. We extend this solution to deal with missing data and redundancy in real world spectrograms, and hence for 2D localization of natural sound sources such as speech. We further generalize the model to the challenging case of multiple sound sources and we propose a variational EM framework. The associated algorithm, referred to as variational EM for source separation and localization (VESSL) yields a Bayesian estimation of the 2D locations and time-frequency masks of all the sources. Comparisons of the proposed approach with several existing methods reveal that the combination of acoustic-space learning with Bayesian inference enables our method to outperform state-of-the-art methods.Comment: 19 pages, 9 figures, 3 table
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