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ShiyangWang929/README.md

Hi, I'm Shiyang 👋

I am an MSc Acoustics and Music Technology student at the University of Edinburgh, with a background in physics and an interest in audio signal processing, acoustics, spatial audio, machine learning, and creative audio programming.

About Me

  • 🎓 Studying MSc Acoustics and Music Technology at the University of Edinburgh
  • 🔊 Interested in audio DSP, spatial audio, room acoustics, and physical modelling
  • 🤖 Exploring applications of machine learning in audio and acoustics
  • 💻 Working mainly with MATLAB, Python, C++, and Max/MSP
  • 🎧 Currently researching mixing time in binaural augmented-reality audio rendering

Technical Skills

Programming: MATLAB, Python, C++, Max/MSP Audio: Digital signal processing, spatial audio, room acoustics, physical modelling, audio synthesis Machine Learning: PyTorch, audio classification, feature extraction and model evaluation Tools: Git, GitHub, Visual Studio, REAPER, LaTeX

Current Focus

My MSc dissertation investigates how source and receiver conditions affect the amount of detailed early-reflection information required in binaural augmented-reality audio rendering. The project combines room-acoustic simulation, binaural impulse responses, acoustic measurements, audio signal processing, and perceptual evaluation.

Featured Projects

An audio machine-learning project investigating the classification of wind instruments using signal-processing features and machine-learning models.

Key areas: audio classification, machine learning, PyTorch, feature extraction

A real-time virtual instrument based on the Karplus–Strong algorithm, developed to explore physical modelling and interactive audio synthesis.

Key areas: C++, audio programming, physical modelling, VST development

An interactive generative music system developed in Max/MSP, translating exoplanet data into musical parameters and real-time sound structures.

Key areas: Max/MSP, data sonification, generative music, interactive audio

A hardware-controlled Python synthesizer using a Raspberry Pi, an MCP23S17 I/O expander, and seven physical pushbuttons.

Key areas: Python, Raspberry Pi, sound synthesis, hardware interaction

Pinned Loading

  1. karplus-strong-synth karplus-strong-synth Public

    Karplus-Strong plucked-string synth (JUCE)

    C++

  2. wind-instrument-classification wind-instrument-classification Public

    Wind instrument classification using handcrafted audio features and CRNN-based spectrogram learning.

    Jupyter Notebook