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Communication Dans Un Congrès Année : 2018

Semi-supervised learning with deep neural networks for relative transfer function inverse regression

Résumé

Prior knowledge of the relative transfer function (RTF) is useful in many applications but remains little studied. In this paper, we propose a semi-supervised learning algorithm based on deep neural networks (DNNs) for RTF inverse regression, that is to generate the full-band RTF vector directly from the source-receiver pose (position and orientation). Two typical scenarios are discussed: training on labeled RTFs only, or on additional unlabeled RTFs. Both setups utilize the low-dimensional manifold property of RTF in stationary environments. With this property as an additional regulariza-tion term, a smooth mapping solution with respect to the manifold is obtained. Experimental simulations show that the proposed method achieves a lower mean prediction error than the free field model with few labeled RTFs, and the unlabeled RTFs are essential in improving the inverse regression performance.
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Dates et versions

hal-01797886 , version 1 (22-05-2018)

Identifiants

  • HAL Id : hal-01797886 , version 1

Citer

Ziteng Wang, Junfeng Li, Yonghong Yan, Emmanuel Vincent. Semi-supervised learning with deep neural networks for relative transfer function inverse regression. ICASSP 2018 – IEEE International Conference on Acoustics, Speech and Signal Processing, Apr 2018, Calgary, Canada. ⟨hal-01797886⟩
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