14 citations · 63 across the 18 of their papers we have counts for
21 papers
VampNet: Music Generation via Masked Acoustic Token Modeling
Hugo Flores Garcia, Prem Seetharaman, Rithesh Kumar +1
We introduce VampNet, a masked acoustic token modeling approach to music synthesis, compression, inpainting, and variation. We use a variable masking schedule during training which…
Cross-domain Neural Pitch and Periodicity Estimation
Max Morrison, Caedon Hsieh, Nathan Pruyne +1
Pitch is a foundational aspect of our perception of audio signals. Pitch contours are commonly used to analyze speech and music signals and as input features for many audio tasks,…
Music Separation Enhancement with Generative Modeling
Noah Schaffer, Boaz Cogan, Ethan Manilow +3
Despite phenomenal progress in recent years, state-of-the-art music separation systems produce source estimates with significant perceptual shortcomings, such as adding extraneous…
Improving Source Separation by Explicitly Modeling Dependencies Between Sources
Ethan Manilow, Curtis Hawthorne, Cheng-Zhi Anna Huang +2
We propose a new method for training a supervised source separation system that aims to learn the interdependent relationships between all combinations of sources in a mixture. Rat…
Reproducible Subjective Evaluation
Max Morrison, Brian Tang, Gefei Tan +1
Human perceptual studies are the gold standard for the evaluation of many research tasks in machine learning, linguistics, and psychology. However, these studies require significan…
Deep Learning Tools for Audacity: Helping Researchers Expand the Artist's Toolkit
Hugo Flores Garcia, Aldo Aguilar, Ethan Manilow +2
We present a software framework that integrates neural networks into the popular open-source audio editing software, Audacity, with a minimal amount of developer effort. In this pa…