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Schedule as of Oct 11, 2022 - subject to change

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Thursday, October 27 • 11:45am - 12:00pm
Stereo InSE-NET: Stereo Audio Quality Predictor Transfer Learned from Mono InSE-NET

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Automatic coded audio quality predictors are typically designed for evaluating single channels without considering any spatial aspects. With InSE-NET [1], we demonstrated mimicking a state-of-the-art coded audio quality metric (ViSQOL-v3 [2]) with deep neural networks (DNN) and subsequently improving it – completely with programmatically generated data. In this study, we take steps towards building a DNN-based coded stereo audio quality predictor and we propose an extension of the InSE-NET for handling stereo signals. The design considers stereo/spatial aspects by conditioning the model with left, right, mid, and side channels; and we name our model Stereo InSE-NET. By transferring selected weights from the pre-trained mono InSE-NET and retraining with both real and synthetically augmented listening tests, we demonstrate a significant improvement of 12% and 6% of Pearson’s and Spearman’s Rank correlation coefficient, respectively, over the latest ViSQOL-v3 [3].

Speakers
avatar for Arijit Biswas

Arijit Biswas

Dolby Germany GmbH
GJ

Guanxin Jiang

Dolby Germany GmbH


Thursday October 27, 2022 11:45am - 12:00pm EDT
Online Papers
  Applications in Audio
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