37 citations · 73 across the 6 of their papers we have counts for
6 papers
Predicting Preferred Dialogue-to-Background Loudness Difference in Dialogue-Separated Audio
Luca Resti, Martin Strauss, Matteo Torcoli +2
Dialogue Enhancement (DE) enables the rebalancing of dialogue and background sounds to fit personal preferences and needs in the context of broadcast audio. When individual audio s…
Improved Normalizing Flow-Based Speech Enhancement using an All-pole Gammatone Filterbank for Conditional Input Representation
Martin Strauss, Matteo Torcoli, Bernd Edler
Deep generative models for Speech Enhancement (SE) received increasing attention in recent years. The most prominent example are Generative Adversarial Networks (GANs), while norma…
A DNN Based Post-Filter to Enhance the Quality of Coded Speech in MDCT Domain
Kishan Gupta, Srikanth Korse, Bernd Edler +1
Frequency domain processing, and in particular the use of Modified Discrete Cosine Transform (MDCT), is the most widespread approach to audio coding. However, at low bitrates, audi…
A Hands-on Comparison of DNNs for Dialog Separation Using Transfer Learning from Music Source Separation
Martin Strauss, Jouni Paulus, Matteo Torcoli +1
This paper describes a hands-on comparison on using state-of-the-art music source separation deep neural networks (DNNs) before and after task-specific fine-tuning for separating s…
A Flow-Based Neural Network for Time Domain Speech Enhancement
Martin Strauss, Bernd Edler
Speech enhancement involves the distinction of a target speech signal from an intrusive background. Although generative approaches using Variational Autoencoders or Generative Adve…
Classification vs. Regression in Supervised Learning for Single Channel Speaker Count Estimation
Fabian-Robert Stöter, Soumitro Chakrabarty, Bernd Edler +1
The task of estimating the maximum number of concurrent speakers from single channel mixtures is important for various audio-based applications, such as blind source separation, sp…