Scholar

Bob L. T. Sturm

Google Scholar ID: KdeYIvMAAAAJ

Associate Professor, KTH Royal Institute of Technology, Stockholm

audio and music signal processingmusic information retrievalmusic generationevaluationsparse approximation

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Citations & Impact

All-time

Citations

2,612

H-index

i10-index

Publications

Co-authors

list available

Contact

GitHubOpen ↗LinkedInOpen ↗

Publications

3 items

Data-Driven Analysis of Text-Conditioned AI-Generated Music: A Case Study with Suno and Udio

2025

Cited

"I made this (sort of)": Negotiating authorship, confronting fraudulence, and exploring new musical spaces with prompt-based AI music generation

2025

Cited

SMART: Tuning a symbolic music generation system with an audio domain aesthetic reward

2025

Cited

Resume

Academic Achievements

Provided a detailed examination of the MusiCNN system, including how it processes audio signals and the impact of different layers on the input data.

Research Experience

Conducted in-depth analysis of the MusiCNN system, exploring its architecture and functionality.

Co-authors

10 total

Saumitra Mishra

Executive Director at JP Morgan

Simon Dixon

Centre for Digital Music, Queen Mary University of London

Emmanouil Benetos

Queen Mary University of London

Mads Græsbøll Christensen

Professor, Dept. of Electronic Systems, Aalborg University

Luca Casini

Postdoctoral researcher in Music and AI at KTH Stockholm

Nicolas Jonason

PhD Student, KTH Royal Institute of Technology

Anna-Kaisa Kaila

PhD Candidate, KTH Royal Institute of Technology

David Dalmazzo

KTH, Speech Music and Hearing. Music Technology Group, Pompeu Fabra University