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
| Name | Project/interests/keywords |
|---|---|
| Berker Banar | Towards Composing Contemporary Classical Music using Generative Deep Learning |
| 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 Reader in Machine Listening, 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 Bromham | The role of nostalga in music production |
| Sungkyun Chang | Deep learning technologies for multi-instrument automatic music transcription |
| Ruby Crocker | Continuous mood recognition in film music |
| Alejandro Delgado | Fine grain time resolution audio features for MIR |
| Prof. Simon Dixon Professor of Computer Science, Deputy Director of C4DM, Director of the AIM CDT, Turing Fellow | 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. |
| Andrew (Drew) Edwards | Deep Learning for Jazz Piano: Transcription + Generative Modeling |
| 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 Foster | Modelling the Creative Process of Jazz Improvisation |
| Iacopo Ghinassi | Semantic understanding of TV programme content and structure to enable automatic enhancement and adjustment |
| Callum Goddard | Deep learning technologies for multi-instrument automatic music transcription |
| Andrea Guidi | Design for auditory imagery |
| Edward Hall | Probabilistic modelling of thematic development and structural coherence in music |
| Jiawen Huang | Lyrics Alignment For Polyphonic Music |
| Thomas Kaplan | Probabilistic modelling of rhythm perception and production |
| Harnick Khera | Informed source separation for multi-mic production |
| Yukun Li | Computational Comparison Between Different Genres of Music in Terms of the Singing Voice |
| Lele Liu | Automatic music transcription with end-to-end deep neural networks |
| Carlos Lordelo | Instrument modelling to aid polyphonic transcription |
| Ilaria Manco | Multimodal Deep Learning for Music Information Retrieval |
| Andrea Martelloni | Real-Time Gesture Classification on an Augmented Acoustic Guitar using Deep Learning to Improve Extended-Range and Percussive Solo Playing |
| 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'Connor | Singing Voice Attribute Transformation |
| Dr Johan Pauwels Lecturer in Audio Signal Processing | automatic music labelling, music information retrieval, music signal processing, machine learning for audio, chord/key/structure (joint) estimation, instrument identification, multi-track/channel audio, music transcription, graphical models, big data science |
| Mary Pilataki | Deep Learning methods for Multi-Instrument Music Transcription |
| Vjosa Preniqi | Predicting demographics, personalities, and global values from digital media behaviours |
| Courtney Reed | Physiological sensing of the singing voice and musical imagery usage in vocalists |
| Xavier Riley | Pitch tracking for music applications - beyond 99% accuracy |
| 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 Sarkar | New perspectives in instrument-based audio source separation |
| Pedro Sarmento | Guitar-Oriented Neural Music Generation in Symbolic Format |
| Elona Shatri | Optical music recognition using deep learning |
| Vinod Subramanian | Note level audio features for understanding and visualising musical performance |
| Cyrus Vahidi | Perceptual end to end learning for music understanding |
| Soumya Sai Vanka | Music Production Style Transfer and Mix Similarity |
| Elizabeth Wilson | Co-creative Algorithmic Composition Based on Models of Affective Response |
| Yixiao Zhang | Machine Learning Methods for Artificial Musicality |
| Jincheng Zhang | Emotion-specific Music Generation Using Deep Learning |
