Guitar Tablature Transcriber & Synthesizer 

Under the guidance of Professor Jeffrey Fessler, I developed of a guitar tablature transcriber synthesizer, an AI-driven tool designed to both convert raw monophonic guitar audio into tablature notation and synthesize guitar-mimicking playback of any inputted tablature. My work focused on refining the transcription pipeline, improving accuracy through deep learning model, integrating real-time processing features, and modelling the ADSR wave format for each guitar frequency on the synthesizer counterpart.

I implemented signal processing techniques to enhance pitch detection and note segmentation, leveraging Fourier and wavelet transforms. Additionally, I optimized the AI model by training it on diverse datasets, improving transcription reliability across different playing styles. To ensure usability, I developed a user interface that provides real-time feedback and MIDI synthesis for immediate playback.

Despite initial challenges in audio signal processing and model generalization, I overcame these obstacles through extensive research and iterative experimentation. This project not only deepened my understanding of machine learning applications in music but also allowed me to engage with interdisciplinary methodologies, bridging engineering and artistic expression.