20 citations · 48 across the 10 of their papers we have counts for
11 papers
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…
Sequence-to-Sequence Piano Transcription with Transformers
Curtis Hawthorne, Ian Simon, Rigel Swavely +2
Automatic Music Transcription has seen significant progress in recent years by training custom deep neural networks on large datasets. However, these models have required extensive…
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…