MAchine Learning and AI
Master's Project / Dissertation
- Industry supported by L-Acoustics, in progress until August 2026.
- Crowdsourcing a captioned dataset of Room Impulse Responses to explore the human perception of acoustics in natural language.
- This research aims to provide data for better fine-tuning and training of automated audio captioning (AAC) models.
- Investigate for any links between the parameters of Impulse Response (RT60, DRR, etc.) and human language.
Highlighted projects
Beat Detection using TCN'sRe-implemented a SOA Beat Detection system and trained it on the Ballroom dataset, achieved ~90% accuracy in beat detection, and extended the system to detect downbeats. Built a spectral analysis dataset pipeline to efficiently train the model.
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Emotion AND Gender ClassificationUsed a custom TCN to learn emotion and gender from VGGish embeddings of the RAVDESS emotion datasets spectral features. Evaluated both models in isolation and together. Had to calculate RF to ensure Classifier properly analyzes VGG embeddings.
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Beat Generation with GAN'sCreated a novel interactive system utilizing a GAN to make an interactive beat generator, with multiple genre selections. Performed a small qualitative evaluation study on beats to determine the systems metrical quality and accruracy in capturing genre.
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