First AIM cohort students:

PhD StudentProject title

Adan Benito		Beyond the fret: gesture analysis on fretted instruments and its applications to instrument augmentation

Berker Banar		Generating emotional music using AI

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 StudentProject title

Benjamin Hayes		Perceptually motivated deep learning approaches to creative sound synthesis

Christian Steinmetz		End-to-end generative modeling of multitrack mixing with non-parallel data and adversarial networks

Corey Ford		Artificial Intelligence for Supporting Musical Creativity and Engagement in Child-Computer Interaction

Eleanor Row		Automatic micro-composition for professional/novice composers using generative models as creativity support tools

Harnick Khera		Informed source separation for multi-mic production
in collaboration with BBC

Jiawen Huang		Lyrics Alignment For Polyphonic Music

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

Luca Marinelli		Gender-coded sound: A multimodal data-driven analysis of gender encoding strategies in sound and music for advertising

Madeline Hamilton		Improving AI-generated Music with Pleasure Models

Max Graf		PERFORM-AI (Provide Extended Realities for Musical Performance using AI)

Shubhr Singh		Audio Applications of Novel Mathematical Methods in Deep Learning

Xavier Riley		Digging Deeper - expanding the “Dig That Lick” corpus with new sources and techniques

Yixiao Zhang		Machine Learning Methods for Artificial Musicality
in collaboration with Apple

Yin-Jyun Luo		Industry-scale Machine Listening for Music and Audio Data
in collaboration with Spotify