no code implementations • 30 Jan 2023 • Chris Donahue, Antoine Caillon, Adam Roberts, Ethan Manilow, Philippe Esling, Andrea Agostinelli, Mauro Verzetti, Ian Simon, Olivier Pietquin, Neil Zeghidour, Jesse Engel
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 music featuring their own voice.
1 code implementation • 28 Sep 2022 • Yusong Wu, Josh Gardner, Ethan Manilow, Ian Simon, Curtis Hawthorne, Jesse Engel
We call this system the Chamber Ensemble Generator (CEG), and use it to generate a large dataset of chorales from four different chamber ensembles (CocoChorales).
1 code implementation • 11 Jun 2022 • Curtis Hawthorne, Ian Simon, Adam Roberts, Neil Zeghidour, Josh Gardner, Ethan Manilow, Jesse Engel
An ideal music synthesizer should be both interactive and expressive, generating high-fidelity audio in realtime for arbitrary combinations of instruments and notes.
3 code implementations • 15 Feb 2022 • Curtis Hawthorne, Andrew Jaegle, Cătălina Cangea, Sebastian Borgeaud, Charlie Nash, Mateusz Malinowski, Sander Dieleman, Oriol Vinyals, Matthew Botvinick, Ian Simon, Hannah Sheahan, Neil Zeghidour, Jean-Baptiste Alayrac, João Carreira, Jesse Engel
Real-world data is high-dimensional: a book, image, or musical performance can easily contain hundreds of thousands of elements even after compression.
Ranked #35 on Language Modelling on WikiText-103
2 code implementations • ICLR 2022 • Josh Gardner, Ian Simon, Ethan Manilow, Curtis Hawthorne, Jesse Engel
Automatic Music Transcription (AMT), inferring musical notes from raw audio, is a challenging task at the core of music understanding.
Ranked #2 on Music Transcription on Slakh2100
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
2 code implementations • 19 Jul 2021 • Curtis Hawthorne, Ian Simon, Rigel Swavely, Ethan Manilow, Jesse Engel
Automatic Music Transcription has seen significant progress in recent years by training custom deep neural networks on large datasets.
1 code implementation • 30 Mar 2021 • Gautam Mittal, Jesse Engel, Curtis Hawthorne, Ian Simon
Score-based generative models and diffusion probabilistic models have been successful at generating high-quality samples in continuous domains such as images and audio.
no code implementations • ICML 2020 • Kristy Choi, Curtis Hawthorne, Ian Simon, Monica Dinculescu, Jesse Engel
We consider the problem of learning high-level controls over the global structure of generated sequences, particularly in the context of symbolic music generation with complex language models.
4 code implementations • ICLR 2019 • Curtis Hawthorne, Andriy Stasyuk, Adam Roberts, Ian Simon, Cheng-Zhi Anna Huang, Sander Dieleman, Erich Elsen, Jesse Engel, Douglas Eck
Generating musical audio directly with neural networks is notoriously difficult because it requires coherently modeling structure at many different timescales.
no code implementations • 11 Oct 2018 • Chris Donahue, Ian Simon, Sander Dieleman
We present Piano Genie, an intelligent controller which allows non-musicians to improvise on the piano.
12 code implementations • ICLR 2019 • Cheng-Zhi Anna Huang, Ashish Vaswani, Jakob Uszkoreit, Noam Shazeer, Ian Simon, Curtis Hawthorne, Andrew M. Dai, Matthew D. Hoffman, Monica Dinculescu, Douglas Eck
This is impractical for long sequences such as musical compositions since their memory complexity for intermediate relative information is quadratic in the sequence length.
Ranked #3 on Music Modeling on JSB Chorales
5 code implementations • 10 Aug 2018 • Sageev Oore, Ian Simon, Sander Dieleman, Douglas Eck, Karen Simonyan
Music generation has generally been focused on either creating scores or interpreting them.
1 code implementation • 1 Jun 2018 • Ian Simon, Adam Roberts, Colin Raffel, Jesse Engel, Curtis Hawthorne, Douglas Eck
Discovering and exploring the underlying structure of multi-instrumental music using learning-based approaches remains an open problem.
1 code implementation • 30 Oct 2017 • Curtis Hawthorne, Erich Elsen, Jialin Song, Adam Roberts, Ian Simon, Colin Raffel, Jesse Engel, Sageev Oore, Douglas Eck
We advance the state of the art in polyphonic piano music transcription by using a deep convolutional and recurrent neural network which is trained to jointly predict onsets and frames.