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Peter van Kranenburg is an Assistant Professor in Music Information Computing at Utrecht University (The Netherlands) and a guest researcher at the KNAW Meertens Institute in Amsterdam. His work bridges computer science and musicology with a focus on computational approaches to music analysis. His research spans computational musicology, computational humanities, and music information retrieval. Van Kranenburg specializes in developing computational models for analyzing musical structures, particularly melodic similarity measures, folk song transmission patterns, and large-scale analysis of song traditions. His work often involves interdisciplinary collaboration between computer scientists and musicologists. His recent publications demonstrate consistent research in computational approaches to music analysis, with a focus on melodic similarity, folk song transmission, and computational modeling of musical traditions. His work spans both technical computer science aspects of music information retrieval and substantive musicological applications. Van Kranenburg has been involved in several significant research projects including the H2020 Polifonia-project (2021-2024), which focused on large scale analysis of European song traditions and curation of historic data on musical instruments, particularly pipe organs. His educational background combines technical and musicological expertise, having earned master's degrees in both Electrical Engineering (Delft University of Technology, 2003) and Musicology (Utrecht University, 2004), followed by a PhD from Utrecht University. His doctoral research developed melodic similarity measures and software tools for analysis of audio recordings of religious chant.

• Computational Musicology
• Computational Humanities
• Music Information Retrieval
• +5 more

Cynthia Liem is an Associate Professor at the Multimedia Computing Group of Delft University of Technology, uniquely bridging classical music and computer science. Her interdisciplinary career includes dual master’s degrees in classical piano performance and computer science, as well as a PhD in Music Information Retrieval (MIR). She focuses on making information accessible while addressing systemic blind spots in AI, data quality, and annotation practices, with applications in classical music, library domains, and fair hiring. Liem also advocates for ethical AI through public debates, notably analyzing algorithmic bias in the childcare benefits scandal. Key Roles: Associate Professor (TU Delft), Co-founder of ICT Next Generation (ICTng) Expertise: Music Information Retrieval, Responsible Artificial Intelligence, Data Validity Research Interests: Liem’s work examines how AI systems can better accommodate nuanced domains like classical music, where traditional metadata frameworks (e.g., artist, album) fail to capture multi-movement compositions and interpretive differences. She extends this to broader societal challenges, including fair data-driven decision-making and reducing translational barriers between technical and non-technical disciplines. Scientific Awards: Google Scholarship for PhD Research Advisory & Societal Impact: Liem actively engages in public discourse on algorithmic accountability and co-founded ICTng to amplify junior ICT academics' voices in national policy discussions. Her career reflects a commitment to interdisciplinary collaboration and ethical technology design.

• Classical Music
• Knowledge Engineering
• Music Information Retrieval
• +6 more

Anja Volk is Professor of Music Information Computing at Utrecht University's Faculty of Science , bridging computer science, mathematics, and musicology. With a dual background in mathematics and musicology, she has worked in Germany (1999-2003), the USA (2003-2005), and the Netherlands (2006-present). Her research focuses on music as a fundamental human trait , with applications in music technology for health and wellbeing, particularly through music-driven serious games and inclusivity research . Recipient of NWO-VIDI grant (2010) Awarded Westerdijk Award (2018) and Utrecht University Diversity and Inclusion Award (2020) Co-founder of Transactions of the International Society for Music Information Retrieval (2018) , Women in MIR (WIMIR) mentoring program (2016) , and Society for Mathematics and Computation in Music (2007) Her recent publications cover topics like computational music analysis for therapy, AI-driven education tracks, and music's role in healthcare applications. She actively contributes to interdisciplinary education through workshops and the master's course Sound and Music Technology , leading to collaborations like the AI Song Contest entries and diverse holiday playlist projects featured in NPO Radio 1 .

• Music Information Retrieval (MIR)
• Computational Musicology
• Mathematical Music Theory
• +7 more