Who am I?

I'm Harry Gray, an Audio Technologist, Creative Developer. My work sits at the intersection of sound, data, and interactive technology, combining audio software development, machine learning, creative coding, and hardware design.I develop real-time audio applications in C++ / Python, build custom electronic systems, and explore how machine learning can analyse, interpret, and generate musical information. My interests span digital signal processing (DSP), music information retrieval,signal processing,sound design, 3D design, and interactive media, with a particular focus on projects that bridge technical and creative disciplines.I have currently graduated with a BSc in Audio and Music Technology with Computing (First Class) at the University of Plymouth, I am focused on the future of intelligent audio systems tools that go beyond processing sound to understand its structure, context, and creative potential within the modern world.

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Course Breakdown
A selection of works from BSc (Hons) Audio and Music Technology with Computing at the University of Plymouth.

Click any card or multiple to learn more about my Degree.
AMT6001
Audio Software Development
  • C++ / JUCE
  • DSP
  • VST3
A JUCE-based ADSR synthesizer plugin, featuring 8-voice polyphony, MIDI input, bit-crush distortion, reverb, and a real-time oscilloscope GUI. Covering implementation of OOP principles, bug fixes, and testing via DAW integration and Pluginval, which the plugin passed successfully.
AMT6001 Github [CLICK ME]
AMT6003
Music Information Retrieval
  • MIR
  • Python
  • GUI
A Python-based Music Information Retrieval tool, using Librosa to extract Beat Tracking, MFCC, STFT, LUFS, Spectral Centroid, and Zero Crossing data from audio files. Results are displayed via a Tkinter GUI with waveform and spectrum graphs, threaded processing, and real-time audio playback.
AMT6003 Github [CLICK ME]
AMT6004
Data Science Ethics
  • MIR
  • Python
  • AI & ML
  • Data Science
A machine learning project, training a Random Forest Regressor on ~183,000 tracks from the MediaEval AcousticBrainz dataset to predict BPM. I built a threaded Python pipeline to process, clean, and convert JSON audio features to CSV, achieving a ~95% improvement over the baseline with a final MAE of 1.058 BPM, alongside ethical analysis of dataset bias toward Western music.
AMT6004MX Github [CLICK ME]
AMT5006
Physical Computing: Creative & Interactive Systems
  • Ardunio
  • Max MSP
  • Audio Hardware
SYNCORA is a wearable ECG-based toolkit I built exploring the relationship between heartrate and sound. Using an ESP32 microcontroller, a Gravity ECG sensor, and a custom 3D-printed enclosure, it streams live heartrate data via Wi-Fi UDP into Max MSP and TouchDesigner, driving real-time audio BPM and generative visuals synchronized to the user's heartbeat.
AMT5006 Presentation [CLICK ME]
AMT5001
Audio Technology Design & Build
  • Ardunio
  • Max MSP
  • Audio Hardware
A Arduino-based drum machine I built to critique the academic devaluation of creative subjects. It runs four sequencers with an OLED UI, rotary encoder, and a deliberately counterproductive "Educate" button, communicating with Max MSP via serial for audio synthesis, all housed in a custom engraved MDF enclosure.
Pebble+ Notes [CLICK ME]
AMT6002
Advanced Audio Production
  • Dolby Atmos
  • Audio Engineering
  • Logic Pro X
An advanced audio production project I completed at the University of Plymouth, producing a Country genre stereo recording and a Dolby Atmos immersive experience in Logic Pro. I applied hybrid microphone techniques across a full drumkit, acoustic guitar, and vocals, followed by multi-stage EQ, compression, and spatial processing, critically evaluating outcomes against professional industry standards.
AMT5002
Live Sound
  • Live
  • Dante
  • Miking
  • Mixing Console
A live sound production essay reflecting on my role in a two-event multi-genre concert at The University of Plymouth's "The House" venue, operating a Yamaha QL5 across five acts spanning gospel, rock, and acoustic. I cover pre-event planning, technical execution, gain staging, monitor management, and team collaboration, critically evaluating what went well and areas for improvement.
Context [CLICK ME]
AMT/MUS 6001
Negotiated Dissertation Project
  • Hardware
  • Sonification
  • Large Datasets
  • Unconventional Computing
I grew oyster mushrooms, wired them up with modified ECG equipment, and turned their bioelectrical signals into sound.
My dissertation explores whether mycelium can meaningfully "compose" music.
AMT6001 Github [CLICK ME]
78%
Highest Grade
1st
Degree Class
2
Award's

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