The utilization of deep learning techniques in generating various contents (such as image,
text, etc.) has become a trend. Especially music, the topic of this paper, has attracted …

The term “differentiable digital signal processing” describes a family of techniques in which
loss function gradients are backpropagated through digital signal processors, facilitating …

An ideal music synthesizer should be both interactive and expressive, generating high-
fidelity audio in realtime for arbitrary combinations of instruments and notes. Recent neural …

Differentiable Wavetable Synthesis (DWTS) is a technique for neural audio synthesis which
learns a dictionary of one-period waveforms ie wavetables, through end-to-end training. We …

We present a wav-to-wav generative model for the task of singing voice conversion from any
identity. Our method utilizes both an acoustic model, trained for the task of automatic speech …

We present the Neural Waveshaping Unit (NEWT): a novel, lightweight, fully causal
approach to neural audio synthesis which operates directly in the waveform domain, with an …

This paper presents FastSVC, a light-weight cross-domain singing voice conversion (SVC)
system, which can achieve high conversion performance, with inference speed 4x faster …

Growing research demonstrates that synthetic failure modes imply poor generalization. We
compare commonly used audio-to-audio losses on a synthetic benchmark, measuring the …

Typically, singing voice conversion (SVC) depends on an embedding vector, extracted from
either a speaker lookup table (LUT) or a speaker recognition network (SRN), to model …

Abstract We present the Latent Timbre Synthesis, a new audio synthesis method using deep
learning. The synthesis method allows composers and sound designers to interpolate and …