13 citations · 21 across the 4 of their papers we have counts for
11 papers
Advances in Thunder Sound Synthesis
Eva Fineberg, Jack Walters, Joshua Reiss
A recent comparative study evaluated all known thunder synthesis techniques in terms of their perceptual realness. The findings concluded that none of the synthesised audio extract…
WaveBeat: End-to-end beat and downbeat tracking in the time domain
Christian J. Steinmetz, Joshua D. Reiss
Deep learning approaches for beat and downbeat tracking have brought advancements. However, these approaches continue to rely on hand-crafted, subsampled spectral features as input…
Randomized Overdrive Neural Networks
Christian J. Steinmetz, Joshua D. Reiss
By processing audio signals in the time-domain with randomly weighted temporal convolutional networks (TCNs), we uncover a wide range of novel, yet controllable overdrive effects.…
Modeling plate and spring reverberation using a DSP-informed deep neural network
Marco A. Martínez Ramírez, Emmanouil Benetos, Joshua D. Reiss
Plate and spring reverberators are electromechanical systems first used and researched as means to substitute real room reverberation. Nowadays they are often used in music product…
A general-purpose deep learning approach to model time-varying audio effects
Marco A. Martínez Ramírez, Emmanouil Benetos, Joshua D. Reiss
Audio processors whose parameters are modified periodically over time are often referred as time-varying or modulation based audio effects. Most existing methods for modeling these…
End-to-End Probabilistic Inference for Nonstationary Audio Analysis
William J. Wilkinson, Michael Riis Andersen, Joshua D. Reiss +2
A typical audio signal processing pipeline includes multiple disjoint analysis stages, including calculation of a time-frequency representation followed by spectrogram-based featur…