Matthew Prockup
Education:
- MS Drexel University (Electrical Engineering)
- BS Drexel University (Electrical Engineering)
Bio:
I am currently a student pursuing my Phd. in Electrical Engineering at Drexel University. I am also an avid percussionist and composer, having performed in and composed for various ensembles large and small. Additionally I am an instructor/arranger for various high school marching music programs on the east coast. The overall scope of my work is to develop technology to aid in the music making process that I love, and love to share with my students.
Research Interests:
Almost all recording and music composition today involves the use of a computer in some way. As a composer and musician, I am interested in new ways to enhance and streamline the composition, recording, and performance process through new technology. My work spans a wide scope of topics including audio signal processing, machine learning, and human computer interaction.
Projects:
- Multi-touch Technology - I worked worked with a self-built multi-touch display, and focused creating applications and SDK's to enhance and streamline the user experience. I worked with pathologists at the University of Pennsylvania and the Children's Hospital of Philadelphia to create a multi-touch medical image slide viewer as well as worked towards creating a simple SDK for allowing non-technical developers to incorporate multi-touch into Adobe FLASH applications.
- iNotes: Orchestral Performance Companion - I am continuing further development on the iNotes Project in collaboration with the Philadelphia Orchestra. We have developed a system that helps users by guiding them through the performance using a handheld application (iPhone app) in real-time. Using audio features, we attempt to align the live performance audio with that of a previously annotated reference recording. The aligned position is transmitted to users’ handheld devices and pre-annotated information about the piece is displayed synchronously.
- Expressive Percussion - Percussion is forced to be more expressive than most people realize because the performer must alter parameters other than pitch, such as speed, volume, timbre, and excitation techniques in order to perform expressively. I plan to study expressive percussion through audio feature analysis, and develop classification techniques and models that will help to better understand the details of expression specific to percussion performance. Currently, I am collaborating with the Music Industry department at Drexel to develop a sample library containing a wide range of percussion instrument articulations as well as a novel tablet computer interface to easily navigate the vast library. In addition to being a stand-alone sample library, it will act as a large dataset of sound samples that I will use for various experiments and classification tasks.