activity
20182023
most citedA general-purpose deep learning approach to model time-varying audio effects

13 citations · 75 across the 22 of their papers we have counts for

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Showing 2018Show all

5 papers · 1 filter

eess.SP2018

Unifying Probabilistic Models for Time-Frequency Analysis

William J. Wilkinson, Michael Riis Andersen, Joshua D. Reiss +2

In audio signal processing, probabilistic time-frequency models have many benefits over their non-probabilistic counterparts. They adapt to the incoming signal, quantify uncertaint…

eess.AS2018★ 2 cited

Modeling of nonlinear audio effects with end-to-end deep neural networks

Marco A. Martínez Ramirez, Joshua D. Reiss

In the context of music production, distortion effects are mainly used for aesthetic reasons and are usually applied to electric musical instruments. Most existing methods for nonl…

eess.IV2018★ 2 cited

An empirical approach to the relationship between emotion and music production quality

David Ronan, Joshua D. Reiss, Hatice Gunes

In music production, the role of the mix engineer is to take recorded music and convey the expressed emotions as professionally sounding as possible. We investigated the relationsh…

eess.AS2018★ 4 cited

Automatic Minimisation of Masking in Multitrack Audio using Subgroups

David Ronan, Zheng Ma, Paul Mc Namara +2

The iterative process of masking minimisation when mixing multitrack audio is a challenging optimisation problem, in part due to the complexity and non-linearity of auditory percep…

cs.LG2018

A Generative Model for Natural Sounds Based on Latent Force Modelling

William J. Wilkinson, Joshua D. Reiss, Dan Stowell

Recent advances in analysis of subband amplitude envelopes of natural sounds have resulted in convincing synthesis, showing subband amplitudes to be a crucial component of percepti…