14 citations · 40 across the 12 of their papers we have counts for
12 papers · 1 filter
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…
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…
Unsupervised Source Separation By Steering Pretrained Music Models
Ethan Manilow, Patrick O'Reilly, Prem Seetharaman +1
We showcase an unsupervised method that repurposes deep models trained for music generation and music tagging for audio source separation, without any retraining. An audio generati…
Leveraging Hierarchical Structures for Few-Shot Musical Instrument Recognition
Hugo Flores Garcia, Aldo Aguilar, Ethan Manilow +1
Deep learning work on musical instrument recognition has generally focused on instrument classes for which we have abundant data. In this work, we exploit hierarchical relationship…
A Study of Transfer Learning in Music Source Separation
Andreas Bugler, Bryan Pardo, Prem Seetharaman
Supervised deep learning methods for performing audio source separation can be very effective in domains where there is a large amount of training data. While some music domains ha…
Bespoke Neural Networks for Score-Informed Source Separation
Ethan Manilow, Bryan Pardo
In this paper, we introduce a simple method that can separate arbitrary musical instruments from an audio mixture. Given an unaligned MIDI transcription for a target instrument fro…