School of Electronic Engineering and Computer Science

Dr Emmanouil Benetos


Senior Lecturer

Telephone: +44 20 7882 6206
Room Number: Engineering, Eng 400
Office Hours: Wednesday 16:00-17:00


Deep Learning for Audio and Music (Postgraduate)

This module, for those who have some prior knowledge of machine learning, focusses on deep learning methods and how they can be used to address many tasks in audio and music. The theory of modern deep neural networks (DNNs) is covered, including training of common DNN types as well as modifying DNNs for new purposes. Various tasks in analysis/generation of audio and music are studied directly to inspire the content, using raw audio and/or symbolic representations. Background in machine learning is essential, and some background in digital signal processing is highly recommended. Music knowledge would be desirable but is not a requirement.

Music Informatics (Postgraduate/Undergraduate)

This module introduces students to state-of-the-art methods for the analysis of music data, with a focus on music audio. It presents in-depth studies of general approaches to the low-level analysis of audio signals, and follows these with specialised methods for the high-level analysis of music signals, including the extraction of information related to the rhythm, melody, harmony, form and instrumentation of recorded music. This is followed by an examination of the most important methods of extracting high-level musical content, sound source separation, and on analysing multimodal music data.


Research Interests:

  • Audio signal processing
  • Machine learning for audio and sequential data
  • Music information retrieval
  • Computational sound scene analysis
  • Computational musicology / ethnomusicology

Research Funding

For a list of funded research projects see:


For a complete list of publications see: