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Department of Computer Science

Music Generation and Information Processing via Deep Learning

Primary supervisor

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Other projects with the same supervisor


  • Competition Funded Project (Students Worldwide)

This research project is one of a number of projects at this institution. It is in competition for funding with one or more of these projects. Usually the project which receives the best applicant will be awarded the funding. Applications for this project are welcome from suitably qualified candidates worldwide. Funding may only be available to a limited set of nationalities and you should read the full department and project details for further information.

Project description

Music appears as an art form and cultural activity whose medium is sound organized in time, which is an ultimate language for human beings. Music is performed with a vast range of instruments and vocal techniques; there are solely instrumental pieces, solely vocal pieces and pieces that combine singing and instruments. Above all, music generation (aka musical composition) is regarded as a creative task by creating a specific style of musical content or writing a new piece of music while there are many other music information processing tasks. For automatic music generation and information processing, algorithmic composition techniques have been developed for several decades. While some progresses were made, there are still many open challenges, e.g., effective music representations and modelling, for automatic music generation and information processing. Deep learning has been proven to be a powerful technique in tackling complex real-world problems. As opposed to handcrafted models, such as grammar-based or rule-based music generation, deep learning techniques allow for automatic music generation via learning a model from an arbitrary corpus. As a result, a single learning model trained on different corpora may be used for various musical genres.

The project is going to investigate and develop novel automatic music generation and information processing techniques with exploring and applying the state-of-the-art generative models such as generative adversarial networks (GAN) and variational auto-encoder (VAE) as well as deep sequence modelling techniques, e.g., transformers,. In this project, main issues to be studied include effective representation of music notes suitable for music content generation, modelling various music structures that effectively express the notions of harmony and melody, specific music style modelling and transfer and effective yet efficient music generation strategies to be used in deep learning and generative models as well as dedicated deep learning models for different music information processing tasks. In particular, this project is suitable for one who is enthusiastic about music and interested in applying the state-of-the-art machine learning techniques in tackling complex real-world problems.

It is worth highlighting that this is an extremely challenging project of a great novelty. In order to take this project, research experience related to the application of related deep learning techniques may be required. It is also essential to be self-motivated and to have decent background knowledge in music theory, mathematics, machine learning as well as good programming skills. Apart from those stated above, it would be ideal that one has knowledge and skills in performing music, e.g., vocal or playing a musical instrument.

Person specification

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Applicants will be required to evidence the following skills and qualifications.

  • This project requires mathematical engagement and ability substantially greater than for a typical Computer Science PhD. Give evidence for appropriate competence, as relevant to the project description.
  • You must be capable of performing at a very high level.
  • You must have a self-driven interest in uncovering and solving unknown problems and be able to work hard and creatively without constant supervision.


Applicants will be required to evidence the following skills and qualifications.

  • You will have good time management.
  • You will possess determination (which is often more important than qualifications) although you'll need a good amount of both.


Applicants will be required to address the following.

  • Comment on your transcript/predicted degree marks, outlining both strong and weak points.
  • Discuss your final year Undergraduate project work - and if appropriate your MSc project work.
  • How well does your previous study prepare you for undertaking Postgraduate Research?
  • Why do you believe you are suitable for doing Postgraduate Research?