Schedule as of Oct 11, 2022 - subject to change

Default Time Zone is EDT - Eastern Daylight Time

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

Sign up or log in to save this to your schedule, view media, leave feedback and see who's attending!

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].

avatar for Arijit Biswas

Arijit Biswas

Dolby Germany GmbH

Guanxin Jiang

Dolby Germany GmbH

Thursday October 27, 2022 11:45am - 12:00pm EDT
Online Papers
  Applications in Audio
  • badge type: ALL ACCESS or ONLINE