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SEFGAN: Harvesting the Power of Normalizing Flows and GANs for Efficient High-Quality Speech Enhancement
This paper proposes SEFGAN, a Deep Neural Network (DNN) combining maximum likelihood training and Generative Adversarial Networks (GANs) for efficient speech enhancement (SE).
Predicting Preferred Dialogue-to-Background Loudness Difference in Dialogue-Separated Audio
When individual audio stems are unavailable from production, Dialogue Separation (DS) can be applied to the final audio mixture to obtain estimates of these stems.
Improved Normalizing Flow-Based Speech Enhancement using an All-pole Gammatone Filterbank for Conditional Input Representation
Deep generative models for Speech Enhancement (SE) received increasing attention in recent years.
A Flow-Based Neural Network for Time Domain Speech Enhancement
Speech enhancement involves the distinction of a target speech signal from an intrusive background.
A Hands-on Comparison of DNNs for Dialog Separation Using Transfer Learning from Music Source Separation
The music separation models are selected as they share the number of channels (2) and sampling rate (44. 1 kHz or higher) with the considered broadcast content, and vocals separation in music is considered as a parallel for dialog separation in the target application domain.
