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Dr Jordan B. L. Smith

Lecturer in Audio Signal Processing

Email: jordan.smith@qmul.ac.uk
Room Number: G. O. Jones Building, Room 410
Website: https://jblsmith.github.io

Profile

Jordan is a Lecturer in Audio Signal Processing in SPCS. He teaches digital audio in the Queen Mary School Hainan programme, and conducts research in co-operation with the Centre for Digital Music (C4DM) in EECS.

 

He earned a BA in Music and Physics at Harvard College, an MA in Music Technology at McGill University, an MSc in Operations Research Engineering at the University of Southern California, and a PhD in Computer Science at QMUL. He has worked at government research labs (3 years at AIST Japan) and industry (3.5 years at TikTok).

Teaching

QHE4101: Introduction to Digital Audio

Research

Research Interests:

Jordan's research career has focused on music structure, the nested patterns of repetition that exist within songs. His work has included: models for predicting the long-term structure of songs; investigations into how listeners make sense of structure; methods of generating music with structure; algorithms for extracting loops and predicting their compatibility; and games and interfaces for remixing songs.

 

His work has appeared in the journals TISMIR, JNMR, Music Theory Online and IEEE Multimedia, and in the proceedings of ISMIR, ICASSP, AAAI, and elsewhere. With his PhD supervisor Prof. Elaine Chew and with Prof. Gerard Assayag, he edited the book Mathemusical Conversations.

 

Publications

Wang, J.-C., Y.-N. Hung, and J. B. L. Smith. 2022. To catch a chorus, verse, intro, or anything else: Analyzing a song with structural functions. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 416–20.

Nieto, O., G. J. Mysore, C.-i. Wang, J. B. L. Smith, J. Schlüter, T. Grill, and B. McFee. 2020. Audio-Based Music Structure Analysis: Current Trends, Open Challenges, and Applications. Transactions of the International Society for Music Information Retrieval 3:1, 246-–63.

Chen, B.-Y., J. B. L. Smith, and Y.-H. Yang. 2020. Neural Loop Combiner: Neural network models for assessing the compatibility of loops. In Proceedings of ISMIR. 424–31. Montreal, QC, Canada.

Smith, J. B. L., Y. Kawasaki, and M. Goto. 2019. Unmixer: An interface for extracting and remixing loops. In Proceedings of ISMIR. 824–31. Delft, Netherlands.

Smith, J. B. L., J. Kato, S. Fukayama, G. Percival, and M. Goto. 2017. The CrossSong Puzzle: Developing a logic puzzle for musical thinking. Journal of New Music Research 46:3. 213–28.

 

Scholarly Contributions

Google Scholar

ORCID

 

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