Authors: Fredrik Cumlin (fcumlin@gmail.com), Xinyu Liang (hopeliang990504@gmail.com)
This is the official implementation of latent distortion classification in non-intrusive speech quality assessment from the paper "Impairments are Clustered in Latents of Deep Neural Network-based Speech Quality Models".
The authors are unable to make the dataset open access due to licensing restrictions. Note that we have used LibriSpeech as a clean speech dataset, and distorted the signals using augmentations from audiomentations.
See https://github.com/karolpiczak/ESC-50.
We have made the codebase gin configurable, which is a lightweight configuration framework. See configs/tot.gin for an example. Example run:
python train.py --gin_path=configs/tot.gin --save_dir=path/to/save_dir
We would like to thank our co-authors of the paper for great insights and discussions (without consideration of order): Victor Ungureanu (ungureanu@google.com), Chandan K.A. Reddy (chandanka@google.com), Christian Schüldt (cschuldt@google.com), and Saikat Chatterjee (sach@kth.se).
We also would like to thank the Knut and Alice Wallenberg Foundation for providing computational resources at the Berzelius cluster at the National Supercomputer Centre.
MIT License
Copyright (c) 2025 Fredrik Cumlin, Royal Institute of Technology
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