Multi-task learning for music information retrieval

Supervisor: Dr Emmanouil Benetos

Research group(s): Centre for Digital Music

Music signals and music representations incorporate and express several concepts: pitches, onsets/offsets, chords, beats, instrument identities, sound sources, and key to name but a few. In the field of music information retrieval, methods for automatically extracting information from audio focus only on isolated concepts and tasks, thus ignoring the interdependencies and connections between musical concepts. Recent advances in machine and deep learning have showed the potential of multi-task learning (MTL), where multiple learning tasks are solved at the same time, while exploiting commonalities and differences across tasks. This research project will investigate methods for multi-task learning for music information retrieval. The successful candidate will investigate, propose and develop novel machine learning methods and software tools for jointly estimating multiple musical concepts from complex audio signals. This will result in improved learning efficiency and prediction accuracy when compared to task-specific models, and will help gain a deeper understanding on the connections between musical concepts.