Instrument-based Audio Source Separation for Performance Augmentation and Assisted Learning

Lead Research Organisation: Queen Mary, University of London
Department Name: Sch of Electronic Eng & Computer Science

Abstract

This PhD project focuses on a well-know challenging problem in the field of music information retrieval (MIR) - automatically separating individual instrument tracks from raw audio data, which allows one to reverse the recording/mixing process of music creation and enables new possibilities for instrument-based content extraction, transcription and augmentation.
The PhD research project will focus on the following tasks and areas:
1) Gaining deep understandings of the state-of-the-art solutions for audio source separation found in both academic research and commercial applications.
2) Developing, applying and optimizing analytical and deep learning models to improve on the performance and accuracy of audio source separation tasks, including vocal separation, and music instruments separation, with a focus on piano, drums and string instrument sounds.
3) Applying the successfully trained models on audio data collected from noisy environment and/or far-field scenarios, and optimise their performance for specific application contexts.
4) Experimenting transcription techniques on the source separated audio data for melody tracking, rhythm detection and MIDI/MPE conversion, collaborating with fellow PhD students and ROLI engineers.
5) Generating both world-class publications and commercial impacts on ROLI's software/hardware products

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S022694/1 01/07/2019 31/12/2027
2268775 Studentship EP/S022694/1 23/09/2019 30/09/2023 Saurjya Sarkar