Jorge Martinez Castaneda

About

"I want to contribute in making our faculty a welcoming place to study, advance science, and work"

I'm an Assistant Professor with focus on Education at the Multimedia Computing Group of the Intelligent Systems department of EEMCS where I do research on improving speech recognition in adverse conditions. I received my PhD degree from TuDelft in 2013 and worked afterwards with research groups from Google and Huawei, as well as in other small companies in the Netherlands. I specialize in acoustical signal processing. In particular modeling and simulation of "multi-channel room impulse responses", which is a characterization of how the sound field evolves in a given place. This has many uses in our modern society, from improved speech recognition in voice- controlled "intelligent personal assistants", to better noise reduction in your mobile phone, or to be able to understand the playing message at the train station even when the train arrives. I currently maintain active collaboration with industrial partners Bang & Olufsen, Alpine, and Kien on related research topics.

I also have a heart for education. I’m motivated into pursuing an education vision that could be described as “emphatic and inclusive scientific education”. A vision that incorporates ways to getting to know and understand better our societal context and ourselves, our students and each other, to more effectively give students the long-lasting skills and values they need to cope with the challenges of modern society. I worked in the past as an educator in our faculty where I could gather insights into the current opportunities and challenges of our educational methods and programs.

Apart from being an educator and a scientist I consider myself an idealist and feminist, dreaming and striving at making education and science accessible and inclusive for everyone. I'm member of the faculty's Diversity and Inclusion Team (EDIT) and I'm member of the faculty's Work's Council (OdC).

Want to talk about any of these subjects? Do not hesitate in contacting me!

Publications

Courses

Courses 2024

Courses 2023

Courses 2022

Courses 2021

Courses 2020

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Courses 2017

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Courses 2013

News

Thesis Projects

Open Proposals

1. Interpreting the sound produced by cows using a potted microphone at the ear

Project Information

Starting: November 2024
Industrial Partner: AGIS INNOVATIONS MMC
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Problem Statement

This thesis focuses on interpreting the sound produced by cows which will give new insights in the well-being of a cow, especially its emotional state.

Objectives

  1. Process noisy audio measurements from a potted microphone, 

  2. Interpret the obtained audio. 

  3. Create your own data set with custom hardware clipped to the cows being held at the farm in Harmelen.

Expected Outcomes
Epand our data set for improved analyses and new diagnostics. 

2. Contact-less palm recognition for Bio-Metric login credential system

Project Information card

Starting: November 2024
Industrial Partner: EYA Solutions
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Problem Statement

This thesis focuses on researching algorithms for video-based contact-less hand-palm recognition, across various lighting conditions with the aim to integrated it into a system that employs biometric authentication methods for auto-login (zero-interaction systems). This system is being developed for Elderly healthcare.

Objectives

  1. Conduct a literature research on state-of-the-art algorithms and applications regarding contact-less biometric data analysis (hand-palm detection) using video live-streams.

  2. Choose on possible algorithms candidates that are suitable for real-time embedded systems running on Single Board Computers (SBC).

  3. Research on improving the state-of-the-art algorithms to make them applicable in at different distances, different environmental light conditions, and crucially, suitable for parallelization, e.g., multiprocessing, multi-threading.
    Additionally, you will show the applicability of your algorithm in a real setup by integrating it into an existing system, and (co)create the necessary modules to achieve this.

Expected Outcomes
The system should be deployable as a subsystem using a shell script and must be isolated by an API from the rest of the system, with which it integrates this as an module into an existing backend and frontend. The algorithm should have a high accuracy and almost real-time performance.

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