181 citations · 661 across the 28 of their papers we have counts for
6 papers · 1 filter
A data acquisition setup for data driven acoustic design
Romana Rust, Achilleas Xydis, Kurt Heutschi +8
In this paper, we present a novel interdisciplinary approach to study the relationship between diffusive surface structures and their acoustic performance. Using computational desi…
GACELA -- A generative adversarial context encoder for long audio inpainting
Andres Marafioti, Piotr Majdak, Nicki Holighaus +1
We introduce GACELA, a generative adversarial network (GAN) designed to restore missing musical audio data with a duration ranging between hundreds of milliseconds to a few seconds…
Adversarial Generation of Time-Frequency Features with application in audio synthesis
Andrés Marafioti, Nicki Holighaus, Nathanaël Perraudin +1
Time-frequency (TF) representations provide powerful and intuitive features for the analysis of time series such as audio. But still, generative modeling of audio in the TF domain…
Audio inpainting of music by means of neural networks
Andrés Marafioti, Nicki Holighaus, Piotr Majdak +1
We studied the ability of deep neural networks (DNNs) to restore missing audio content based on its context, a process usually referred to as audio inpainting. We focused on gaps i…
Improving DNN-based Music Source Separation using Phase Features
Joachim Muth, Stefan Uhlich, Nathanael Perraudin +3
Music source separation with deep neural networks typically relies only on amplitude features. In this paper we show that additional phase features can improve the separation perfo…
Inpainting of long audio segments with similarity graphs
Nathanael Perraudin, Nicki Holighaus, Piotr Majdak +1
We present a novel method for the compensation of long duration data loss in audio signals, in particular music. The concealment of such signal defects is based on a graph that enc…