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:My current research interest is in automatic music transcription, more general interest in music information retrieval, symbolic music modelling, automatic music composition, and natural language processing.
- Liu L, Morfi V, Benetos E (2021), ACPAS: A Dataset of Aligned Classical Piano Audio and Scores for Audio-to-Score Transcription Late-Breaking Demo Session of the 22nd Int. Society for Music Information Retrieval Conference
- Liu L, Benetos E (2021), From Audio to Music Notation $nameOfConference
- Liu L, Morfi G-V, Benetos E (2021), Joint multi-pitch detection and score transcription for polyphonic piano music IEEE International Conference on Acoustics, Speech and Signal Processing
- Liu L, Morfi G-V, Benetos E (2020), Joint Piano-roll and Score Transcription for Polyphonic Piano Music DMRN+15: Digital Music Research Network One-day Workshop
- Ycart A, Liu L, Benetos E et al. (2020), Investigating the Perceptual Validity of Evaluation Metrics for Automatic Piano Music Transcription $nameOfConferenceDOI: 10.5334/tismir.57
- Ycart A, Liu L, Benetos E et al. (2020), Musical Features for Automatic Music Transcription Evaluation $nameOfConference
- Liu L, Benetos E (2019), Automatic Music Accompaniment with a Chroma-based Music Data Representation DMRN+14: Digital Music Research Network One-day Workshop