2 citations · 2 across the 3 of their papers we have counts for
4 papers · 1 filter
End-to-end LPCNet: A Neural Vocoder With Fully-Differentiable LPC Estimation
Krishna Subramani, Jean-Marc Valin, Umut Isik +2
Neural vocoders have recently demonstrated high quality speech synthesis, but typically require a high computational complexity. LPCNet was proposed as a way to reduce the complexi…
HpRNet : Incorporating Residual Noise Modeling for Violin in a Variational Parametric Synthesizer
Krishna Subramani, Preeti Rao
Generative Models for Audio Synthesis have been gaining momentum in the last few years. More recently, parametric representations of the audio signal have been incorporated to faci…
VaPar Synth -- A Variational Parametric Model for Audio Synthesis
Krishna Subramani, Preeti Rao, Alexandre D'Hooge
With the advent of data-driven statistical modeling and abundant computing power, researchers are turning increasingly to deep learning for audio synthesis. These methods try to mo…
Generative Audio Synthesis with a Parametric Model
Krishna Subramani, Alexandre D'Hooge, Preeti Rao
Use a parametric representation of audio to train a generative model in the interest of obtaining more flexible control over the generated sound.