Jean-marc Valin
Senior Staff Research Scientist at Google
Montreal, Quebec, Canada
Summary
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Rockstar🎓
Top SchoolJean-Marc Valin is a Senior Staff Research Scientist based in Montreal with 24 years of experience applying machine learning to speech and signal processing, and a Ph.D. in Electrical Engineering. He has a proven track record of inventing efficient, deployable ML algorithms and driving production-grade systems at companies including Google, AWS, and Mozilla. A long-time open-source contributor to projects like Speex, Opus and rnnoise, his codec and denoising work is embedded in billions of devices and blends classical signal processing with neural approaches (e.g., RNN VADs and band-based gain denoisers). He specializes in speech/audio coding, differentiable signal processing, real-time communications and acoustic cancellation, and is noted for pragmatic innovations that improve quality under bandwidth and latency constraints.
24 years of coding experience
16 years of employment as a software developer
PhD Electrical Engineering, PhD Electrical Engineering at Université de Sherbrooke
French, English
Stackoverflow
302reputation
56kreached
21answers
0questions
Github Skills (21)
fftw10
signal-processing10
machine-learning10
rnn-model10
audio-processing10
c1110
n10
c1710
fft10
codec10
avcodec10
python9
gru8
android-ndk6
speex6
Programming languages (8)
Github contributions (5)
xiph/opus
May 2011 - Jan 2023
Modern audio compression for the internet.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:9 reviews, 1001 commits, 52 PRs in 11 years 10 months
Contributions summary:Jean-marc's contributions primarily focused on improving the Opus audio codec, specifically addressing rate allocation for stereo SILK in hybrid mode. They modified the encoder to allocate more bits to the SILK layer and reduce the narrowing threshold, increasing audio quality. The user also added a Recurrent Neural Network (RNN) for Voice Activity Detection (VAD) and speech/music classification, implemented using dense layers and a GRU layer, demonstrating an interest in applying machine learning for audio processing. Further improvements included fixing bandwidth detection for 24 kHz analysis, and fixing CELT PLC and providing more detailed fixes.
audiocompressioncodecc
xiph/rnnoise
Jul 2017 - Oct 2017
Recurrent neural network for audio noise reduction
Role in this project:Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:1 release, 70 commits, 6 PRs in 2 months
Contributions summary:Jean-marc made substantial contributions to the audio noise reduction project, developing core functionalities of the denoiser. Their work included implementing forward and inverse Fourier transforms, analysis and synthesis components, and a band-based gain adjustment mechanism. The user integrated a Viterbi-based VAD (Voice Activity Detection) to improve the training data by reducing the amount of noise. They also worked on feature extraction, and applied a perceptual exponent during the training, all with the goal of improving the performance of the denoising model.
audiorecurrent-neural-networksrnnnoise-reductionc
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