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Christian Schüldt

4 accepted papers

2025

Impairments are Clustered in Latents of Deep Neural Network-based Speech Quality Models

ICASSP 2025accepted

In this article, we provide an experimental observation: Deep neural network (DNN) based speech quality assessment (SQA) models have inherent latent representations where many types of impairments are clustered. While DNN-based SQA models are not trained for impairment classification, our experiment…

Cited by 0SourceScholar
2020

Performance Study of a Convolutional Time-Domain Audio Separation Network for Real-Time Speech Denoising

ICASSP 2020accepted

Time-domain audio separation networks based on dilated temporal convolutions have recently been shown to perform very well compared to methods that are based on a time-frequency representation in speech separation tasks, even outperforming an oracle binary time-frequency mask of the speakers. This p…

Cited by 0SourceScholar