29 citations · 31 across the 5 of their papers we have counts for
6 papers · 1 filter
FlowMAC: Conditional Flow Matching for Audio Coding at Low Bit Rates
Nicola Pia, Martin Strauss, Markus Multrus +1
This paper introduces FlowMAC, a novel neural audio codec for high-quality general audio compression at low bit rates based on conditional flow matching (CFM). FlowMAC jointly lear…
SEFGAN: Harvesting the Power of Normalizing Flows and GANs for Efficient High-Quality Speech Enhancement
Martin Strauss, Nicola Pia, Nagashree K. S. Rao +1
This paper proposes SEFGAN, a Deep Neural Network (DNN) combining maximum likelihood training and Generative Adversarial Networks (GANs) for efficient speech enhancement (SE). For…
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 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…