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Music Informatics

With online music stores offering millions of songs to choose from, users need assistance. Using digital signal processing, machine learning, and the semantic web, our research explores new ways of intelligently analysing musical data, and assists people in finding the music they want.

We have developed systems for automatic playlisting from personal collections (SoundBite), for looking inside the audio (Sonic Visualiser), for hardening/softening transients, and many others. We also regularly release some of our algorithms under Open Source licences, while maintaining a healthy portfolio of patents.

This area is led by Dr Simon Dixon. Projects in this area include:

  • mid-level music descriptors: chords, keys, notes, beats, drums, instrumentation, timbre, structural segmentation, melody
  • high-level concepts for music classification, retrieval and knowledge discovery: genre, mood, emotions
  • Sonic Visualser
  • semantic music analysis for intelligent editing
  • linking music-related information and audio data
  • interactive auralisation with room impulse responses

PhD Study - interested in joining the team? We are currently accepting PhD applications.


Francisco Rodríguez AlgarraIntelligent Music Machine Listening
Dr Mathieu Barthet
Dr Emmanouil Benetos
RAEng Research Fellow, Lecturer
Music signal analysis, automatic music transcription, acoustic scene analysis, machine learning for audio analysis, computational musicology
Gary Bromham
Tian ChengMulti-instrument Music Transcription using Non-negative Matrix Decomposition Methods and Physical Models
Keunwoo ChoiMusic Information Retrieval, Deep Learning, Music Recommendation
Emmanouil (Emmanuel) Theofanis ChourdakisMachine Learning for Music Audio Analysis and DAFx control, Computer Assisted Multimedia Production, Large Scale Audio and Video Analysis.
Magdalena ChudyMusic Performer Recognition Using Timbre Features
Jiajie DaiModelling Intonation and Interaction in Vocal Ensembles
Dr Simon Dixon
Reader, Deputy Director of C4DM, Director of Graduate Studies
Music informatics, music signal processing, artificial intelligence, music cognition; extraction of musical content (e.g. rhythm, harmony, intonation) from audio signals: beat tracking, audio alignment, chord and note transcription, singing intonation; using signal processing approaches, probabilistic models, and deep learning.
Dr Gyorgy Fazekas
Katerina KostaMathematical models for musical performance analysis
Beici Liang
Dr Matthias Mauch
music transcription (chords, beats, drums, melody, ...), interactive music annotation, singing research, research in the evolution of musical styles
Maria PanteliSignal Processing and Data Mining Tools for the Analysis of Musical Evolution
Elio QuintonRhythm, MIR, Meter, music signal processing
Prof Mark Sandler
Dr Rodrigo Schramm
Visiting Academic
music information retrieval, signal processing, computer music
Dr Bob Sturm
Evaluation, Audio and music signal processing, Machine listening
Dr. Florian Thalmann
Lasse VetterDeep Neural Networks, Data Augmentation, and Audio DSP
Siying WangImproving Accuracy and Robustness in Music Alignment, Multiple Sequence Alignment, Asynchronous Alignment
Dr. Thomas Wilmering
Simin Yang
Adrien YcartMusic Language Models for Audio Analysis
Delia Fano YelaSignal Processing and Machine Learning Methods for Noise and Interference Reduction in Studio and Live Recordings
Steven hargreavesAcquisition and provision of large-scale music meta and feature data

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