PhD Students

First AIM cohort students:

PhD Student	Project title
Adan Benito	Beyond the fret: gesture analysis on fretted instruments and its applications to instrument augmentation
Berker Banar	Towards Composing Contemporary Classical Music using Generative Deep Learning
Marco Comunità	Machine learning applied to sound synthesis models
David Foster	Modelling the Creative Process of Jazz Improvisation
Lele Liu	Automatic music transcription with end-to-end deep neural networks
Ilaria Manco	Deep learning and multi-modal models for the music industry in collaboration with Universal Music Group
Andrea Martelloni	Real-Time Gesture Classification on an Augmented Acoustic Guitar using Deep Learning to Improve Extended-Range and Percussive Solo Playing
Mary Pilataki-Manika	Polyphonic Music Transcription using Deep Learning in collaboration with Apple
Saurjya Sarkar	New perspectives in instrument-based audio source separation
Pedro Sarmento	Musical smart city in collaboration with Holonic Systems Oy.
Elona Shatri	Optical music recognition using deep learning in collaboration with Steinberg Media Technologies GmbH
Cyrus Vahidi	Perceptual end to end learning for music understanding in collaboration with MUSIC Tribe Brands UK Limited

Second AIM cohort students:

PhD Student	Project title
Corey Ford	Artificial Intelligence for Supporting Musical Creativity and Engagement in Child-Computer Interaction
Max Graf	AI-Based Musical Co-Creation in Extended Realities: PERFORM-AI
Madeline Hamilton	Improving AI-generated Music with Pleasure Models
Benjamin Hayes	Perceptually motivated deep learning approaches to creative sound synthesis
Jiawen Huang	Lyrics Alignment For Polyphonic Music
Harnick Khera	Informed source separation for multi-mic production in collaboration with BBC
Yin-Jyun Luo	Industry-scale Machine Listening for Music and Audio Data in collaboration with Spotify
Luca Marinelli	Gender-coded sound: A multimodal data-driven analysis of gender encoding strategies in sound and music for advertising
Xavier Riley	Digging Deeper - expanding the “Dig That Lick” corpus with new sources and techniques
Eleanor Row	Automatic micro-composition for professional/novice composers using generative models as creativity support tools
Shubhr Singh	Audio Applications of Novel Mathematical Methods in Deep Learning
Christian Steinmetz	End-to-end generative modeling of multitrack mixing with non-parallel data and adversarial networks
Jingjing Tang	End-to-End System Design for Music Style Transfer with Neural Networks
Lewis Wolstanholme	Real-time instrument transformation and augmentation with deep learning
Yixiao Zhang	Machine Learning Methods for Artificial Musicality in collaboration with Apple