Deepak Baby is with the Amazon Alexa team as an Applied Scientist since October 2020.
He was previously working as a post-doctoral researcher with Prof. Hervé Bourlard at Idiap Research Institute between May 2019 and June 2020. Prior to that he was a post-doctoral researcher with Prof. Sarah Verhulst at WAVES, Department of Information Technology, Ghent University between February 2017 and April 2019, where he was working on DNN-based hearing restoration strategies for hearing impairment.
Deepak Baby received his PhD in Electrical Engineering (Thesis title : Non-negative Sparse Representations for Speech Enhancement and Recognition) from ESAT/PSI group in KU Leuven in November 2016 under the supervision of Prof. Hugo Van hamme. Deepak received the Masters degree in Communication Engineering from Dept. of Electrical Engineering, Indian Institute of Technology Bombay in 2012 and Bachelors degree in Electronics and Communication Engineering from College of Engineering Trivandrum, India in 2009.
PhD in Electrical Engineering, 2016
KU Leuven, Belgium
MTech in Communication Engineering, 2012
IIT Bombay, India
BTech in Electronics and Communication Engineering, 2009
College of Engineering Trivandrum, India
Working on the ERC project RobSpear developing deep learning aided strategies for correcting non-linear hearing deficits. Responsibilities include:
2018 – 2019 : Arthur Van Den Broucke, MS Student, Ghent University, Belgium
Masters Thesis : Deep learning techniques for individualized auditory profiles
2014 – 2015 : Joris Verhaegen, MS Student, KU Leuven, Belgium
Masters Thesis: NMF-based reduction of background sounds in TV shows for better ASR
Summer 2015 : Shubham Sharma, Summer Intern, KU Leuven, Belgium
Topic: De-reverberation using non-negative matrix factorisation
Summer 2012 : Rainu V. Philip, Anjaly Elias, Summer Interns, IIT Bombay, India
Topic: Face detection using sparse representations based on compressive sensing algorithms
A convolutional neural-network model of human cochlear mechanics and filter tuning for real-time applications
Tricks to improve SEGAN performance. Eveything is re-implemented into Keras with Tensorflow backend.
A fully convolutional end-to-end speech enhancement system with GANs
MATLAB impelementation of a joint dereverberation and denoising algorithm based on non-negative matrix deconvolution