About Me
Hello! I’m Marc, a Digital Signal Processing (DSP) Engineer
My motivation for music began when I started to attend to violin classes at the age of four. After a few years I entered to the Conservatory of Mallorca where I attended music lessons for over ten years. During those years, and probably motivated by the arrival of the first computer at home, I developed a passion for technology too.
Developing a career that merged those two passions has always been my main goal, find below the steps I took.
Experience
Embedded DSP algorithm development, implementation and optimisation. Working on the Embedded DSP team, maintaining and optimising current algorithms, modelling and developing new models while also supporting algorithm design, UI and creative teams.
DSP algorithm development, implementation and optimisation. This included both high-level modeling (Matlab, C/C++, VST plugins) and assembler level optimisation on the target platforms. Also assisting in project planning, scoping and problem solving in terms of software.
Orchestrating development and implementation of audio algorithms. Implementing DSP code for both Meridian Core products and collaborative LG Electronics products. Maintaining and developing the whole Meridian software ecosystem, from the embedded software host to the actual DSP code.
Working at the Computer Science - Storage Systems department, on the European IOStack project. The main objective was to create software-defined Storage (SDS) toolkit for Big Data on top of the OpenStack platform, enabling efficient execution of virtualized analytics applications over virtualized storage resources.
Created an app to detect imperfections on metal sintered pieces as part of AMES quality system based on acquisition of vibration signals of the pieces, signal processing and classification algorithm based on Neural Networks.
Education
Universitat Pompeu Fabra
MSc in Sound and Music Computing
2016 - 2017
Practical and theoretical approaches in topics such as computational modeling, audio engineering, perception, cognition, and interactive systems, the program gives the scientific and technological background needed to start a research or professional career in audio computing. From the generation and analysis of sounds to their transmission and perception, and its analysis from a technical and computational point of view.
Universitat Politècnica de Catalunya
BSc in Audiovisuals Systems Engineering, Telecommunications Engineering
2010 - 2015
Fundamentals and applications of audio, video and multimedia systems and acquisition techniques for the analysis and synthesis of electrical and electronic circuits and digital and analogue communications. Specialization in acoustics and sound systems, digital signal processing, communication systems, electronic equipment and devices and multimedia techniques.
Publications
Computational modelling of expressive music performance in hexaphonic guitar.
Master's Thesis in Sound and Music Computing, Sep 2017
Computational modelling of expressive music performance in hexaphonic guitar.
Proc. of the 10th International Workshop of Machine learning and music, Barcelona, Spain. Oct 2017
Improving OpenStack Swift interaction with the I/OStack to enable Software Defined Storage.
EEE SC2-2017. The 7th IEEE International Symposiumon Cloud and Service Computing,Kanazawa, Japan, Nov 2017
Technical and Personal Skills
Languages
- Catalan, Spanish, English (Cambridge Certificate in Advance English, Jun 2015)
Programming Languages
- Proficient in: C/C++, Python, Matlab
- Medium ability with: JavaScript, Java, Bash, SQL, PHP, Android SDK, R.
Frameworks
- JUCE SDK, SHARC DSP, Motorola DSP, ARM Cortex-M, PureData, Max, SigmaStudio.
Skills
- Digital Signal Processing, Audio Processing, Audio Electronics, Audio Plugins Development, Machine Learning, Pattern Recognition, Bio-metrics, Acoustics, Music Technology, Music Recording, Music Production, GIT Version Control, Linux systems, LaTex.
On-Line Courses:
- Audio Coding: Beyond MP3. Universitat Politècnica de València, edX, Sep 2017
- Machine Learning for musicians and artists. Goldsmiths University of London, Kadenze, Jun 2017
- Audio Signal Processing for music applications. Universitat Pompeu Fabra & Stanford University, Coursera, Sep 2016
- Machine Learning. Stanford University, Coursera, Jun 2016