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cs.SD2026
SCRAPL: Scattering Transform with Random Paths for Machine Learning
Christopher Mitcheltree, Vincent Lostanlen, Emmanouil Benetos +1
The Euclidean distance between wavelet scattering transform coefficients (known as paths) provides informative gradients for perceptual quality assessment of deep inverse problems…
cs.SD2025
Modulation Discovery with Differentiable Digital Signal Processing
Christopher Mitcheltree, Hao Hao Tan, Joshua D. Reiss
Modulations are a critical part of sound design and music production, enabling the creation of complex and evolving audio. Modern synthesizers provide envelopes, low frequency osci…
cs.SD2025
Neutone SDK: An Open Source Framework for Neural Audio Processing
Christopher Mitcheltree, Bogdan Teleaga, Andrew Fyfe +4
Neural audio processing has unlocked novel methods of sound transformation and synthesis, yet integrating deep learning models into digital audio workstations (DAWs) remains challe…