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

Ziteng Wang 1 Junfeng Li 1 Yonghong Yan 1 Emmanuel Vincent 2 
2 MULTISPEECH - Speech Modeling for Facilitating Oral-Based Communication
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : 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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Submitted on : Tuesday, May 22, 2018 - 8:31:53 PM
Last modification on : Saturday, June 25, 2022 - 7:43:21 PM
Long-term archiving on: : Tuesday, September 25, 2018 - 10:49:02 AM


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  • HAL Id : hal-01797886, version 1


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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