About me
- Alexandre Défossez, defossez at meta.com.
- Research scientist at Meta AI / FAIR Paris.
-
Formerly
CIFRE PhD student at FAIR Paris and
Sierra at INRIA Paris,
under the supervision of Léon Bottou (FAIR), Nicolas Usunier (FAIR) and Francis Bach (INRIA).
-
[scholar]
[github]
[twitter]
Interests
Convex and non convex optimization, stochastic gradient methods, source separation, audio processing and synthesis.
Amateur DJ and composer [artist website].
Publications
-
audio
Hybrid Transformers for Music Source Separation.
ICASSP 2023.
[paper]
[code]
S. Rouard, F. Massa, A. Défossez.
-
audio
High Fidelity Neural Audio Compression.
Preprint 2022.
[paper]
[code]
[samples]
A. Défossez*, J. Copet*, G. Synnaeve**, Y. Adi**.
-
neuro
Decoding speech from non-invasive brain recordings.
preprint 2022.
[paper]
A. Défossez, C. Caucheteux, J. Rapin, O. Kabeli, J.R. King.
-
audio
AudioGen: Textually Guided Audio Generation.
ICLR 2023.
[paper]
F. Kreuk, G. Synnaeve, A. Polyak, U. Singer, A. Défossez, J. Copet, D. Parikh, Y. Taigma, Y. Adi.
-
theory
Differentiable Model Compression via Pseudo Quantization Noise.
TMLR 2022.
[paper]
[code]
A. Défossez*, Y. Adi*, G. Synnaeve.
-
theory
A Simple Convergence Proof of Adam and Adagrad. TMLR 2022.
[paper]
A. Défossez, L. Bottou, F. Bach, N. Usunier.
-
neuro
Deep Recurrent Encoder: an end-to-end network to model magnetoencephalography at scale.
NBDT 2022.
[paper]
[code]
O. Chehab, A. Défossez, J.C. Loiseau, A. Gramfort, J.R. King.
-
audio
Implicit Neural Spatial Filtering for Multichannel Source Separation in the Waveform Domain.
Interpseech 2022.
[paper]
D. Markovic, A. Défossez*, A. Richard.
-
audio
Hybrid Spectrogram and Waveform Source Separation.
MDX Workshop, ISMIR 2021.
[paper]
[code]
[samples]
A. Défossez.
-
audio
Real Time Speech Enhancement in the Waveform Domain.
Interspeech 2020.
[paper]
[audio samples]
[code]
A. Défossez, G. Synnaeve, Y. Adi.
-
audio
Music Source Separation in the Waveform Domain. Preprint 2019.
[paper]
[github]
[audio samples]
A. Défossez, N. Usunier, L. Bottou, F. Bach.
-
audio
Demucs: Deep Extractor for Music Sources with extra unlabeled data remixed. Preprint 2019.
[paper]
A. Défossez, N. Usunier, L. Bottou, F. Bach.
-
audio
Regression versus classification for neural network based audio source localization.
WASPAA 2019.
[paper]
L. Perotin, A. Défossez, E. Vincent, R. Serizel, A. Guérin
-
audio
SING:
Symbol-to-Instrument Neural Generator. NIPS 2018.
[paper]
[github]
[poster] [audio
samples]
[slides].
A. Défossez, N. Zeghidour, N. Usunier, L. Bottou, F. Bach.
-
theory
AdaBatch: Efficient Gradient Aggregation Rules for Sequential and Parallel
Stochastic Gradient Methods. Preprint 2017.
[paper].
A. Défossez, F. Bach.
-
theory
Constant step size least-mean-square: Bias-variance trade-offs and optimal
sampling distributions.
AI Stats 2015.
[AI Stats version],
[arXiv version].
A. Défossez, F. Bach.
Software
- EnCodec:
state-of-the-art neural audio codec. The best codec around, especially
for music at 48 kHz :)
- Demucs:
Music source separation, winning model from the Sony 2021 MDX challenge. Can separate drums, bass, and vocals
from the rest of the accompaniment. Jaime Altozano loves it!
- Julius:
Efficient implementations of classical Digital Signal Processing algorithms in PyTorch,
fully differentiable and with CUDA support. Resampling, FFT based convolutions,
FIR low pass filters and decomposition of a signal over multiple frequency bands in the
waveform domain are implemented.
- Denoiser:
Real time speech denoising in the waveform domain. Can be used with Zoom or other
VC software with a virtual soundcard (e.g. Soundflower on a Mac). Live demo :)
Teaching
2022
Gave one lecture on Deep Learning at Scale at Mines Paritech for the PSL week on Large-Scale Machine Learning.
The slides and code are available on the lesson github.
2018
Teaching assistant for the
Deep Learning: Do-It-Yourself!
class at Ecole Normale Superieure:
Misc.
I wrote my PhD manuscript on the
Optimization of Fast Deep Learning Network for Audio Analysis and Synthesis.
Half of it is on audio synthesis and source separation, and the other half is on adaptive and
stochastic optimization.