Jordan B. L. Smith

Jordan B. L. Smith is a Lecturer in Audio Signal Processing at Queen Mary University of London's School of Physical and Chemical Sciences. He teaches digital audio courses in the Queen Mary School Hainan program, requiring annual 2-week trips to China. His research focuses on music structure analysis, music decomposition, and interactive tools for musical creativity.

Education:

• BA in Music and Physics (Harvard College)
• MA in Music Technology (McGill University)
• MSc in Operations Research Engineering (University of Southern California)
• PhD in Computer Science (Queen Mary University of London)

Research Interests: Jordan explores nested patterns in musical structure, listener perception of structure, algorithmic music generation, and interfaces for music remixing. His work bridges computer science and musicology, with contributions to tools like Unmixer (loop extraction) and CrossSong (musical logic puzzles).

Publications Trends: His work emphasizes music structure segmentation, computational creativity, and human-audio interaction. Recent studies focus on scalable music generation (e.g., SymPAC) and improving structural analysis with semi-supervised methods (e.g., MuSFA).

Awards & Collaborations: No explicit awards listed, but collaborations include AIST Japan, IRCAM Paris, and TikTok. Past roles include postdoc at AIST (2014–2017) and industry work at TikTok (2018–2022).

Labs & Teams: Collaborates with the Centre for Digital Music (C4DM) in EECS. Engages in interdisciplinary projects combining music theory, AI, and user interaction design.