20 citations · 93 across the 19 of their papers we have counts for
20 papers
Live Music Models
Lyria Team, Antoine Caillon, Brian McWilliams +33
We introduce a new class of generative models for music called live music models that produce a continuous stream of music in real-time with synchronized user control. We release M…
SingSong: Generating musical accompaniments from singing
Chris Donahue, Antoine Caillon, Adam Roberts +8
We present SingSong, a system that generates instrumental music to accompany input vocals, potentially offering musicians and non-musicians alike an intuitive new way to create mus…
Redefining Relationships in Music
Christian Detweiler, Beth Coleman, Fernando Diaz +13
AI tools increasingly shape how we discover, make and experience music. While these tools can have the potential to empower creativity, they may fundamentally redefine relationship…
The Chamber Ensemble Generator: Limitless High-Quality MIR Data via Generative Modeling
Yusong Wu, Josh Gardner, Ethan Manilow +3
Data is the lifeblood of modern machine learning systems, including for those in Music Information Retrieval (MIR). However, MIR has long been mired by small datasets and unreliabl…
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…
Multi-instrument Music Synthesis with Spectrogram Diffusion
Curtis Hawthorne, Ian Simon, Adam Roberts +4
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…
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…
MIDI-DDSP: Detailed Control of Musical Performance via Hierarchical Modeling
Yusong Wu, Ethan Manilow, Yi Deng +6
Musical expression requires control of both what notes are played, and how they are performed. Conventional audio synthesizers provide detailed expressive controls, but at the cost…
MT3: Multi-Task Multitrack Music Transcription
Josh Gardner, Ian Simon, Ethan Manilow +2
Automatic Music Transcription (AMT), inferring musical notes from raw audio, is a challenging task at the core of music understanding. Unlike Automatic Speech Recognition (ASR), wh…
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…
audioLIME: Listenable Explanations Using Source Separation
Verena Haunschmid, Ethan Manilow, Gerhard Widmer
Deep neural networks (DNNs) are successfully applied in a wide variety of music information retrieval (MIR) tasks but their predictions are usually not interpretable. We propose au…
Towards Musically Meaningful Explanations Using Source Separation
Verena Haunschmid, Ethan Manilow, Gerhard Widmer
Deep neural networks (DNNs) are successfully applied in a wide variety of music information retrieval (MIR) tasks. Such models are usually considered "black boxes", meaning that th…
Simultaneous Separation and Transcription of Mixtures with Multiple Polyphonic and Percussive Instruments
Ethan Manilow, Prem Seetharaman, Bryan Pardo
We present a single deep learning architecture that can both separate an audio recording of a musical mixture into constituent single-instrument recordings and transcribe these ins…
Cutting Music Source Separation Some Slakh: A Dataset to Study the Impact of Training Data Quality and Quantity
Ethan Manilow, Gordon Wichern, Prem Seetharaman +1
Music source separation performance has greatly improved in recent years with the advent of approaches based on deep learning. Such methods typically require large amounts of label…
WHAM!: Extending Speech Separation to Noisy Environments
Gordon Wichern, Joe Antognini, Michael Flynn +5
Recent progress in separating the speech signals from multiple overlapping speakers using a single audio channel has brought us closer to solving the cocktail party problem. Howeve…
Streamlined Calibrations of the ATLAS Precision Muon Chambers for Initial LHC Running
N. Amram, R. Ball, Y. Benhammou +17
The ATLAS Muon Spectrometer is designed to measure the momentum of muons with a resolution of dp/p = 3% and 10% at 100 GeV and 1 TeV momentum respectively. For this task, the spect…