Emmanouil Benetos is Senior Lecturer at the School of Electronic Engineering and Computer Science of Queen Mary University of London and Turing Fellow at The Alan Turing Institute. Within Queen Mary, he is member of the Centre for Digital Music, Centre for Intelligent Sensing, Institute of Applied Data Science, and Digital Environment Research Institute, and co-leads the School's Machine Listening Lab.
His main research topic is computational audio analysis, also referred to as machine listening or computer audition - applied to music, urban, everyday and nature sounds. Between 2015-2020 he was Royal Academy of Engineering Research Fellow and has been principal- and co-investigator for several audio-related funded research projects on topics related to sound scene analysis, music information retrieval, and digital musicology. He is also investigator for the MIP-Frontiers European Training Network and the UKRI Centre for Doctoral Training in Artificial Intelligence and Music (AIM).
Data Mining (Postgraduate)
Data that has relevance for decision-making is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations such as the Internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, and electronic patient records. Data mining is a rapidly growing field that is concerned with developing techniques to assist decision-makers to make intelligent use of these repositories. The field of data mining has evolved from the disciplines of statistics and artificial intelligence. This module will combine practical exploration of data mining techniques with a exploration of algorithms, including their limitations. Students taking this module should have an elementary understanding of probability concepts and some experience of programming.
Electronic Engineering Mathematics 2 (Undergraduate)
Module will cover topics in engineering mathematics relevant to Electronics and Electrical Engineering programs: Vector Calculus (field theory, surface and volume integration, field operators), linear algebra (matrices and matrix operations, applications to systems of equations, reduced Row Echelon Form, determinants, Cramer's rule, eigenvalues and eigenvectors), differential equations (solving first and second order DEs).
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.
- Audio signal processing
- Machine learning for audio and sequential data
- Music information retrieval
- Computational sound scene analysis
- Computational musicology / ethnomusicology