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 2023Show all

5 papers · 1 filter

eess.AS2023

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…

cs.SD2023

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…

cs.SD2023★ 3 cited

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…

cs.SD2023★ 1 cited

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…

cs.CL2023★ 1 cited

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…