Thomas Le Cornu

TL;DR: The proposed method aims to estimate a spectral enve- lope from visual features which is then combined with an arti- ficial excitation signal and used within a model of speech pro- duction to reconstruct an audio signal.

TL;DR: The SeedGerm system, which combines cost-effective hardware and open-source software for large-scale germination experiments, automated seed imaging, and machine-learning based phenotypic analysis, could have wide utilities in large- scale seed phenotyping and testing, for both research and routine seed technology applications.

TL;DR: This paper is concerned with generating intelligible audio speech from a video of a person talking, and regression and classification methods are proposed first to estimate static spectral envelope features from active appearance model visual features and two further methods are developed to incorporate temporal information into the prediction.

TL;DR: An automated and scalable field phenotyping platform called CropQuant, designed for easy and cost-effective deployment in different environments, is presented and results generated in 2015 and 2016 field experiments are reported, including developmental profiles of five wheat genotypes, performance-related traits analyses, and new biological insights emerged.

TL;DR: Speech quality and intelligibility tests reveal substantial improvements in the target speech when applying the visual-only and audio-only masks, but with highest performance occurring when combining audio and visual information to create the audio-visual masks.