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Automatic Music Generation via Deep Learning

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Competition Funded PhD Project (Students Worldwide)
    Competition Funded PhD Project (Students Worldwide)

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. For automatic music generation, 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. 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 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 such as memory augmented recurrent neural networks. 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. 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.

If you are interested in this project, please first visit my research student page: http://staff.cs.manchester.ac.uk/~kechen/ for the required materials and information prior to contacting me.

How good is research at University of Manchester in Computer Science and Informatics?

FTE Category A staff submitted: 44.86

Research output data provided by the Research Excellence Framework (REF)

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