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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.

Members

NameProject/interests/keywords
Ruchit AgrawalAdaptive Semi-Supervised Music Alignment
Berker BanarGenerating emotional music using AI
Dr Mathieu Barthet
Senior Lecturer in Digital Media
Music information research, Internet of musical things, Extended reality, New interfaces for musical expression, Semantic audio, Music perception (timbre, emotions), Audience-Performer interaction, Participatory art
Dr Emmanouil Benetos
Senior Lecturer, Turing Fellow
Machine listening, music information retrieval, computational sound scene analysis, machine learning for audio analysis, language models for music and audio, computational musicology
Gary BromhamThe role of nostalga in music production
Emmanouil Theofanis ChourdakisAutomatic Storytelling with Audio
Alejandro DelgadoFine grain time resolution audio features for MIR
Emir DemirelRepresentation Learning in Singing Voice
Prof. Simon Dixon
Professor, Deputy Director of C4DM, Director of the AIM CDT
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 George Fazekas
Senior Lecturer
Semantic Audio, Music Information Retrieval, Semantic Web for Music, Machine Learning and Data Science, Music Emotion Recognition, Interactive music sytems (e.g. intellignet editing, audio production and performance systems)
David FosterModelling the Creative Process of Jazz Improvisation
Yukun LiComputational Comparison Between Different Genres of Music in Terms of the Singing Voice
Beici LiangPiano playing technique detection, multimodal music information retrieval
Lele LiuAutomatic music transcription with end-to-end deep neural networks
Carlos LordeloInstrument modelling to aid polyphonic transcription
Ilaria MancoDeep learning and multi-modal models for the music industry
Dr Matthias Mauch
Visiting Academic
music transcription (chords, beats, drums, melody, ...), interactive music annotation, singing research, research in the evolution of musical styles
Brendan O'ConnorVoice Transformation
Mary Pilataki-ManikaPolyphonic Music Transcription using Deep Learning
Prof Mark Sandler
C4DM Director, Turing Fellow, Royal Society Wolfson Research Merit award holder
Digital Signal Processing, Digital Audio, Music Informatics, Audio Features, Semantic Audio, Immersive Audio, Studio Science, Music Data Science, Music Linked Data.
Saurjya SarkarNew perspectives in instrument-based audio source separation
Dalia SenvaityteAudio Source Separation for Advanced Digital Audio Effects
Elona ShatriOptical music recognition using deep learning
Vinod SubramanianNote level audio features for understanding and visualising musical performance
Cyrus VahidiPerceptual end to end learning for music understanding
Dr Thomas Wilmering
Simin YangAnalysis and Prediction of Listeners' Time-varying Emotion Responses in Live Music Performance
Adrien YcartMusic Language Models for Audio Analysis : neural networks, automatic music transcription, symbolic music modelling

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