13 citations · 75 across the 22 of their papers we have counts for
23 papers
Optimization Techniques for a Physical Model of Human Vocalisation
Mateo Cámara, Zhiyuan Xu, Yisu Zong +2
We present a non-supervised approach to optimize and evaluate the synthesis of non-speech audio effects from a speech production model. We use the Pink Trombone synthesizer as a ca…
General Purpose Audio Effect Removal
Matthew Rice, Christian J. Steinmetz, George Fazekas +1
Although the design and application of audio effects is well understood, the inverse problem of removing these effects is significantly more challenging and far less studied. Recen…
Vocal Tract Area Estimation by Gradient Descent
David Südholt, Mateo Cámara, Zhiyuan Xu +1
Articulatory features can provide interpretable and flexible controls for the synthesis of human vocalizations by allowing the user to directly modify parameters like vocal strain…
Modulation Extraction for LFO-driven Audio Effects
Christopher Mitcheltree, Christian J. Steinmetz, Marco Comunità +1
Low frequency oscillator (LFO) driven audio effects such as phaser, flanger, and chorus, modify an input signal using time-varying filters and delays, resulting in characteristic s…
deep learning of segment-level feature representation for speech emotion recognition in conversations
Jiachen Luo, Huy Phan, Joshua Reiss
Accurately detecting emotions in conversation is a necessary yet challenging task due to the complexity of emotions and dynamics in dialogues. The emotional state of a speaker can…
Modelling black-box audio effects with time-varying feature modulation
Marco Comunità, Christian J. Steinmetz, Huy Phan +1
Deep learning approaches for black-box modelling of audio effects have shown promise, however, the majority of existing work focuses on nonlinear effects with behaviour on relative…
Style Transfer of Audio Effects with Differentiable Signal Processing
Christian J. Steinmetz, Nicholas J. Bryan, Joshua D. Reiss
We present a framework that can impose the audio effects and production style from one recording to another by example with the goal of simplifying the audio production process. We…
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…
Steerable discovery of neural audio effects
Christian J. Steinmetz, Joshua D. Reiss
Applications of deep learning for audio effects often focus on modeling analog effects or learning to control effects to emulate a trained audio engineer. However, deep learning ap…
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…
Neural Synthesis of Footsteps Sound Effects with Generative Adversarial Networks
Marco Comunità, Huy Phan, Joshua D. Reiss
Footsteps are among the most ubiquitous sound effects in multimedia applications. There is substantial research into understanding the acoustic features and developing synthesis mo…
Direct design of biquad filter cascades with deep learning by sampling random polynomials
Joseph T. Colonel, Christian J. Steinmetz, Marcus Michelen +1
Designing infinite impulse response filters to match an arbitrary magnitude response requires specialized techniques. Methods like modified Yule-Walker are relatively efficient, bu…
Efficient neural networks for real-time modeling of analog dynamic range compression
Christian J. Steinmetz, Joshua D. Reiss
Deep learning approaches have demonstrated success in modeling analog audio effects. Nevertheless, challenges remain in modeling more complex effects that involve time-varying nonl…
Guitar Effects Recognition and Parameter Estimation with Convolutional Neural Networks
Marco Comunità, Dan Stowell, Joshua D. Reiss
Despite the popularity of guitar effects, there is very little existing research on classification and parameter estimation of specific plugins or effect units from guitar recordin…
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…
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…
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…
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…
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…
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…