Portrait de Anna (Cheng-Zhi) Huang

Anna (Cheng-Zhi) Huang

Membre industriel principal
anna.huang@mila.quebec
Chaire en IA Canada-CIFAR
Professeure associée, Université de Montréal, Département d'informatique et de recherche opérationnelle
Chercheuse scientifique, Google Brain
Site web
Google Scholar
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Biographie

Anna Huang est chercheuse chez Google DeepMind, où elle travaille sur le projet Magenta. Elle est également professeure adjointe à l'Université de Montréal. Ses recherches portent sur la conception de modèles génératifs et d'interfaces pour soutenir la création musicale et, plus généralement, le processus créatif. Son travail se situe à l'intersection de l'apprentissage automatique, de l'interaction humain-machine et de la musique. Elle est la créatrice de Music Transformer et de Coconet. Coconet est le modèle d'apprentissage automatique qui a alimenté le premier Doodle IA de Google, Doodle Bach, qui a harmonisé en deux jours 55 millions de mélodies provenant d'utilisateurs du monde entier. Ces dernières années, elle a été organisatrice et juge du concours international AI Song Contest et rédactrice invitée pour le numéro spécial de TISMIR sur l'IA et la créativité musicale. Elle est titulaire d'un doctorat de l'Université Harvard, d'une maîtrise du MIT Media Lab et d'une double licence en informatique et en composition musicale de l'Université de la Californie du Sud.

Étudiants actuels

Nithya Shikarpur
Maîtrise recherche - Université de Montréal
nithya.shikarpur@mila.quebec
Site web
Github
Google Scholar
Yusong Wu
Doctorat - Université de Montréal
Co-superviseur⋅e :
Aaron Courville
wu.yusong@mila.quebec

Publications

Grammar Generative Models for Music Notation
Anna (Cheng-Zhi) Huang
Deep generative models have been successfully applied in many learning experiments with digital data, such as images or audio. In the field … (voir plus)of music, they can also be used to generate symbolic representations, in the context of problems such as automatic music generation or transcription [1-3]. A significant challenge for generating structured symbolic data in general is obtaining well-formed results. This is especially true in the case of music. It is indeed widely accepted that musical notation represents, well beyond simple sequences of notes, a hierarchical organization of melodic and harmonic information, inducing non-local dependencies between musical objects [4]. A good representation of this information is essential for the interpretation and analysis of music pieces.
2023-01-01
(publié)
www.semanticscholar.org
Improving Source Separation by Explicitly Modeling Dependencies between Sources
Ethan Manilow
Curtis Hawthorne
Anna (Cheng-Zhi) Huang
Bryan Pardo
Jesse Engel
We propose a new method for training a supervised source separation system that aims to learn the interdependent relationships between all c… (voir plus)ombinations of sources in a mixture. Rather than independently estimating each source from a mix, we reframe the source separation problem as an Orderless Neural Autoregressive Density Estimator (NADE), and estimate each source from both the mix and a random subset of the other sources. We adapt a standard source separation architecture, Demucs, with additional inputs for each individual source, in addition to the input mixture. We randomly mask these input sources during training so that the network learns the conditional dependencies between the sources. By pairing this training method with a blocked Gibbs sampling procedure at inference time, we demonstrate that the network can iteratively improve its separation performance by conditioning a source estimate on its earlier source estimates. Experiments on two source separation datasets show that training a Demucs model with an Orderless NADE approach and using Gibbs sampling (up to 512 steps) at inference time strongly outperforms a Demucs baseline that uses a standard regression loss and direct (one step) estimation of sources.
2022-05-23
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (publié)
doi.org
arxiv.org
Improving Source Separation by Explicitly Modeling Dependencies between Sources
Ethan Manilow
Curtis Hawthorne
Anna (Cheng-Zhi) Huang
Bryan A. Pardo
Jesse Engel
We propose a new method for training a supervised source separation system that aims to learn the interdependent relationships between all c… (voir plus)ombinations of sources in a mixture. Rather than independently estimating each source from a mix, we reframe the source separation problem as an Orderless Neural Autoregressive Density Estimator (NADE), and estimate each source from both the mix and a random subset of the other sources. We adapt a standard source separation architecture, Demucs, with additional inputs for each individual source, in addition to the input mixture. We randomly mask these input sources during training so that the network learns the conditional dependencies between the sources. By pairing this training method with a blocked Gibbs sampling procedure at inference time, we demonstrate that the network can iteratively improve its separation performance by conditioning a source estimate on its earlier source estimates. Experiments on two source separation datasets show that training a Demucs model with an Orderless NADE approach and using Gibbs sampling (up to 512 steps) at inference time strongly outperforms a Demucs baseline that uses a standard regression loss and direct (one step) estimation of sources.
2022-03-28
ArXiv (preprint)
doi.org
arxiv.org