Labs at ISMIR2021 Lab Showcase
1. AIST (Media Interaction Group) | National Institute of Advanced Industrial Science and Technology
PI: Masataka Goto
Since Masataka Goto is a big fan of ISMIR, he and his colleagues attend ISMIR every year since ISMIR 2002, and enjoyed organizing ISMIR 2009 in Japan. We have accepted many interns, hired postdoctoral researchers, and collaborated with other labs. Please come to our booth and chat with us! We are working on various MIR topics such as music analysis/synthesis, active music listening interface, music web service, music recommendation, singing information processing, and lyrics information processing. Our papers have been accepted at ISMIR every year since ISMIR 2001 (https://dblp.org/search?q=goto ismir), and we would like to continue to contribute to this community and the advancement of music technologies!
2. ATIC-amuses | Universidad de Málaga
PI: Lorenzo J. Tardon / Isabel Barbancho
The ATIC group of the University of Malaga has a laboratory in which to carry out its research activity with specialized equipment and material. The laboratory is located in the E.T.S.I. Telecommunication of the University of Malaga.
EEG + NIRS measurement equipment
There are 4 complete systems for the acquisition of electroencephalogram (EEG) signals, actiCHamp Plus from Brain Products. With these, up to 96 EEG channels can be recorded in 4 people at the same time, simultaneously and synchronized between the 4 subjects and also synchronized with the stimulus or activity of which the brain activity of 4 people is being evaluated.
The system is modular and configurable, so that, if required, the number of EEG channels can be increased until it is possible to record 160 channels in the appropriate configuration in two people simultaneously or up to 128 channels in three people.
NIRS kits are also available, including:
• A fixed NIRS measuring device with 32 sources x 32 LED light detectors, compatible with EEG. This system will be expandable to at least 64 sources and 32 detectors. • Two portable NIRS measurement equipment. Each NIRS portable measurement equipment must include at least 16 light sources + 16 light detectors and be compatible with EEG measurements. The two computers can be connected to each other, so that they can be used as a single 32x32 system, or as two totally independent systems of at least 16x16.
The EEG caps and electrodes in this system are compatible with the capture of NIRS signals (Near Infrared Spectroscopy), so that, in the same cap, both EEG electrodes and NIRS optodes can be placed that allow the simultaneous use of both techniques on the same subject.
Audio recording and editing equipment and software:
• Digidesign 003 Rack Audio Card. • Furman protection unit. • Microphones: Neumann (2), Shure (2), Audiotechnic (4 + 2), Contact microphones. • Sennheiser (2) and AudioTechnica monitoring headphones. • Yamaha H550M Studio Monitors. • Zoom H6 portable recorder. • Roland portable recorder. • Roland Quad-capture card. • MIDI Keyboards A-800 Pro Cakewalk by Roland and ALESIS Q49. • YAMAHA MG166 CX-USB mixer. • Audio recording and editing software Pro-Tools, Logic PRO, etc.
• Piano Kawai Awagrand Pro II. • Alhambra 6P and 2P guitars. • Acoustic guitar. • YAMAHA electric guitar. • Fiddle. • Transverse flute. • Flamenco cajon. • OscarSchmidt Autoharp.
Music and voice databases:
• RWC Music Research Data Base. • McGill sound data base. • Database of Piano Chords (Springer). • Other spoken voice databases.
PC and MAC type computers, scanner, printer, Ultimaker S3 3D printer, Gigabyte AORUS GTX 1080 external graphics card, Nikon D60 camera + tripod + ball head, Kinect sensor, Tektronix 2245A oscilloscope, function generator, Power supply, HAMEG multimeter, station Rework station AM6800 soldering iron, Alpha Tools SB401 drilling machine, 2 Lego Mindstorm, 2 iPad, Yamaha DBR12 powered speaker, etc.
3. Algomus | Université de Lille, CRIStAL
PI: Mathieu Giraud
Algomus (Algorithmic Musicology, www.algomus.fr) is a research team in Musical Information Retrieval (MIR) and Computational Music Analysis, focusing on high-level modeling of music scores. We work on analyzing, visualizing, and generating music, for music theorists, musicians, music lovers, and everyone. Collaborating with music theorists, we focus on symbolic scores of tonal music – such as in baroque, classical, romantic, jazz, pop, and dance repertoires. We model patterns, melodies, harmony (chords, chord progressions, cadences), rhythms, texture. We ultimately aim to model music structure. Over the years, the team worked on musical forms such as fugues, variations, and sonata forms. We combine musicological knowledge and computer science methods in text algorithmics, data mining, and machine learning. We are interested in designing explainable algorithmic and AI methods. We are located in France, in the Université de Lille, with strong links to the Université de Picardie Jules-Verne, in Amiens, and the Université de Rouen-Normandie.
4. Audio Data Analysis and Signal Processing (ADASP) | Télécom Paris / Institut Polytechnique de Paris
PI: Slim Essid
The ADASP group develops data analysis methods primarily targeting audio data. These developments rely on signal processing and machine learning techniques, focusing on:
- data decomposition and representation learning methods,
- as well as parametric modelling methods.
Such methods are employed essentially to address two types of tasks:
- source separation,
- human activity-related scene and content analysis, notably using classification methods;
with applications in:
- machine listening,
- music information retrieval,
- audio signal transformation (denoising, enhancement, dereverberation, spatialisation),
- heterogeneous, multiview or multimodal data analysis, especially multimedia content analysis,
- physiological signal analysis, especially M/EEG data, dereverberation, spatialisation.
More on https://adasp.telecom-paris.fr.
5. Audio Information Research (AIR) Lab | University of Rochester
PI: Zhiyao Duan
Country: United States
At the AIR lab, we conduct research in the emerging field of computer audition, i.e., designing computational systems that are able to understand sounds including music, speech, and environmental sounds. We address fundamental issues such as parsing polyphonic auditory scenes as well as developing novel applications such as human-computer collaborative music making systems. We also combine audio processing with the processing of other modalities including video and symbolic data. On music information retrieval (MIR), we have been working on music transcription, source separation, music alignment, audiovisual analysis of music performances, music generation, and interactive music systems. Beyond MIR, we also work on speech separation, enhancement, diarization, verification, sound event detection and sound retrieval.
6. BLACK BOX | German Research Center for AI (DFKI)
PI: Dr. Stephan Baumann
The BLACK BOX is a brandnew lab doing interdisciplinary research about affective and emotional response to AI-generated music. We conduct first experiments using devices to measure biophysical signals (e.g EDA, HRV with the EMPATICA wristband) of listeners and asking in parallel for their perceived emotions (e.g Valence Arousal, GEMS, etc.). We aim at providing a dataset for other researchers and using this dataset to explore deep networks for emotion classification tasks. In addition we are interested how this research might affect new generative models for music generation (e.g. VAEs, CANs, etc.) and co-creation opportunities for artists. The team is an interdisciplinary mixture of cognitive scientists, music retrieval and AI experts as well as musicians, composers interested in the usage of AI.
7. Centre for Digital Music | Queen Mary University of London
PI: Mark Sandler and Simon Dixon
Country: United Kingdom
The Centre for Digital Music was established at Queen Mary University of London in 2003, just two years after Mark Sandler, Mark Plumbley, Josh Reiss (then a post-doc) and another 7 researchers moved from King’s College London. Since then it has grown to be the UK’s foremost university research lab in Music Informatics and more generally in Music Technology and Science. With some 80 members (faculty including 5 full professors , post-docs and phd students) it is active in Augmented Instruments, Machine Listening, Music Cognition, Sound Synthesis and Sound Engineering, all of relevance to ISMIR attendees. Alumni work at most of the major digital music companies (Apple, TenCent, Spotify, Universal …), several with BBC R&D, SMEs across Europe and beyond, and with many universities (NYU, Singapore, and many more). With a long list of major grant awards over the years, the current portfolio includes funding for 60 PhD students in AI and Music.
8. Computer Music Lab | Cargenie Mellon University
PI: Roger B. Dannenberg
Country: United States
The Computer Music Lab at CMU develops computer music and interactive performance technology to enhance human musical experience and creativity. This interdisciplinary effort draws on Music Theory, Cognitive Science, Artificial Intelligence and Machine Learning, Human Computer Interaction, Real-Time Systems, Computer Graphics and Animation, Multimedia, Programming Languages, and Signal Processing.
9. Digital Audio Processing Lab | I.I.T. Bombay
PI: Prof. Preeti Rao
The Digital Audio Processing Lab is involved in research for speech and audio applications. Research projects involving Ph.D. and Masters students include music content analysis and retrieval, speech prosody for language learning, speech enhancement and recognition. Defining computational problems of interest in studies of Indian art music has been a focus of much of the MIR work. The interdisciplinary flavour of several of the projects has stimulated interactions with musicians, musicologists and language experts. Further, audio IP developed in the lab has been incorporated into a few products and services for the entertainment industry. For more information: https://www.ee.iitb.ac.in/student/~daplab/
10. Digital and Cognitive Musicology Lab (DCML) | École Polytechnique Fédérale de Lausanne (EPFL)
PI: Martin Rohrmeier
The Digital and Cognitive Musicology Lab (DCML) is a multi-disciplinary lab based at EPFL in Lausanne, Switzerland. Our team consists of researchers from a wide range of academic fields: from music theory and musicology, to computer science and machine learning, to psychology and cognitive science. Our research reflects these diverse backgrounds, drawing inspiration and innovation from our wide range of knowledge and experience to uncover insights to interesting, fundamental, multi-disciplinary questions. Among our many projects, highlights include the Swiss National Science Foundation funded “Distant Listening to Harmony” project, which aims to leverage a large corpus of expert-annotated harmonic analyses from across many musical epochs both to improve the computational modelling of harmony and to investigate variance in the usage of harmonic patterns across time, and the European Research Council funded “Principles of Musical Structure Building” project, which aims to advance the understanding of the human cognitive capacity to represent and process the complex auditory sequences and syntactic structures found in music. We are happy to talk to you about these or any other of our projects and research interests, as well as answer any other questions you may have about our lab.
11. Distributed Digital Music Archives and Libraries | McGill University
PI: Prof. Ichiro Fujinaga
We focus on developing and evaluating practices, frameworks, and tools for the design and construction of worldwide distributed digital music archives and libraries. Over the last few millennia, humans have amassed an enormous amount of information and cultural material that is scattered around the world. It is becoming abundantly clear that the optimal path for acquisition is to distribute the task of digitizing the wealth of historical and cultural heritage material that exists in analogue formats, which may include books, manuscripts, music scores, maps, photographs, videos, analogue tapes, and phonograph records.
In order to achieve this goal, libraries, museums, and archives throughout the world, large or small, need well-researched policies, proper guidance, and efficient tools to digitize their collections and to make them available economically. The research conducted within the program will address unique and imminent challenges posed by the digitization and dissemination of music media.
12. Gotham Lab | T.U. Dortmund
PI: Prof. Mark Gotham
On 1.10.2021, I begin a professorship in music theory at T.U. Dortmund. There I will create a new lab for computational approaches to music theory, analysis and composition. At least 1–2 positions are available for a first cohort of lab members at the PhD or Postdoc level immediately.
This new lab joins and complements a wider cluster of activity in MIR at T.U. Dortmund, notably including the simultaneous hiring of another professor (Hauke Egermann) and his creation of a group focussing on music psychology and Technology (the Dortmund Systematic Musicology Lab). Both groups join a long-established, wider network of related scholars in music data analysis (sig-ma.de).
While primarily based in Dortmund, the lab will also benefit from a second home in the Department of Computer Science and Technology at the University of Cambridge where I remain an ‘Affiliated Lecturer’.
13. Institute of Computational Perception | Johannes Kepler University Linz
PI: Prof. Gerhard Widmer and Prof. Markus Schedl
The focus of our institute is on Artificial Intelligence and Machine Learning applied to intelligent audio, music, and text processing. We develop and study computational models and algorithms that permit computers to perceive and ‘understand’ aspects of the external world, where we interpret ‘perception’ in the widest sense of the word, as the extraction of useful high-level information and knowledge from complex, possibly low-level data (audio, video, image, text, sensor data, and user-item interaction data). Our current research covers the full range of Music Information Retrieval, with an emphasis on modeling expressive music performance and on recommender systems. Our goal is to offer state-of-the-art research and teaching in these areas, and to provide a teaching environment that permits students to get involved in real research projects as early as possible.
14. Interactive Audio Lab | Northwestern University
PI: Prof. Bryan Pardo
Country: United States
Northwestern University’s Interactive Audio Lab (https://interactiveaudiolab.github.io) is headed by Prof. Bryan Pardo. The lab develops new methods in Machine Learning, Signal Processing and Human Computer Interaction to make new tools for understanding and manipulating sound, with a strong focus on music. Ongoing research in the lab includes audio scene labeling, audio source separation, inclusive interfaces for music creation, generative modeling, new audio production tools and machine audition models that learn without supervision. Past graduates of the lab include many active researchers in the MIR community including Prof. Zhiyao Duan of U Rochester (Scientific Chair for ISMIR 2021), Prof. Mark Cartwright of NJIT (Co-chair DCASE 2019), and Zafar Rafii of Gracenote (Organizer of the ongoing Bish/Bash events). The next generation of researchers coming out of the lab promise to be every bit as impactful.
15. International Audio Laboratories Erlangen | Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Fraunhofer Institute for Integrated Circuits IIS
PI: Bernhard Grill
Being the birthplace of mp3 and AAC, Erlangen has a long history in audio coding and signal processing research going back to the late 1970s. Continuing this tradition, the International Audio Laboratories Erlangen (AudioLabs) were founded in 2008 as a joint institute of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and the Fraunhofer Institute for Integrated Circuits IIS. FAU currently provides five professorship positions while Fraunhofer IIS fuels the AudioLabs with its undisputed know-how in audio engineering and its world-class multimedia infrastructure, including sound labs and a digital cinema lab, and also provides funding. Today, a team of 50 globally-renowned scientists, post-docs, and Ph.D. students from Fraunhofer IIS and FAU conducts cutting-edge research and shapes the future of audio and multimedia. They bring together scientific ingenuity and the know-how to make new ideas a commercial success. Within the AudioLabs, Prof. Meinard Müller (one of the five AudioLabs/FAU professors) is the chair holder for the group on Semantic Audio Processing. His main research interests and teaching activities are in the fields of music information retrieval (MIR), music processing, and audio signal processing. In our lab showcase, we are happy to introduce the general mission of the AudioLabs while having a focus on our MIR-related activities in research and education.
16. JimiLab | Ithaca College
PI: Doug Turnbull
Country: United States
We are currently focused on music recommender systems to help support local music communities. This includes both Localify.org and Porchfest projects.
17. MERI (Music Engagement Research Initiative) | Stanford University
PI: Jonathan Berger (PI), Chris Chafe
Country: United States
We seek to increase our understanding of how and why humans engage with music. Our research integrates:
1) Industrial datasets detailing musical performance, audition, and discovery;
2) Imaging studies aimed at determining the neural correlates of music engagement; and
3) machine-learning experiments aimed at improving how human-computer interfaces (HCI) augment musical expressivity, creation and consumption.
18. Mistic | University of Victoria
PI: Prof. George Tzanetakis
The MISTIC lab directed by Dr. Tzanetakis conducts interdisciplinary research in music information retrieval and more generally computer analysis of music and sound. It focus on computer science aspects of MIR with specific expertise in digital signal processing, machine learning, and human-computer interaction. The lab has strong connections to other units on campus such as the School of Music and the Electrical and Computer Engineering department. It is also connected with the unique combined computer science/music degree offered at the University of Victoria. A large number of undergraduate, graduate, postdocs and visitors have been part of the lab that has been operating for over 15 years. Victoria is a beautiful city on the west coast of Canada and is home to a surprising number of music technology companies that are connected to the lab.
19. Music Computing and Psychology Lab | University of York
PI: Tom Collins
Country: United Kingdom
Both in the lab and with collaborators across the globe, we apply the scientific method to explore 1) the Web Audio API and resultant possibilities for musical creation, consumption, and collaboration, 2) machine learning applied to music and game audio, including but not limited to automatic generation of stylistic compositions, incorporation in software, and the technology’s effect on users and their work, 3) discovery of repeated patterns in music, visual, and other domains, 4) natural language processing and natural language understanding for editing and querying music scores, 5) music cognition, especially musical expectancy and computational modelling.
20. Music Informatics Group | Georgia Institute of Technology
PI: Alexander Lerch
Country: United States
The Music Informatics Group, hosted at the Georgia Tech Center for Music Technology, researches machine learning methods for music analysis and generation, creating the next generation of analysis technology, extracting and interpreting both score-like and performance-driven aspects of musical recordings. Our research covers the areas of Music Information Retrieval, Audio Content Analysis, Music Performance Analysis, and Meta-creation of Music.
21. Music Technology Group | Universitat Pompeu Fabra
PI: Xavier Serra
The Music Technology Group (MTG) of the Universitat Pompeu Fabra in Barcelona, part of its Department of Information and Communication Technologies, carries out research on topics such as audio signal processing, music information retrieval, musical interfaces, and computational musicology. The MTG wants to contribute to the improvement of the information and communication technologies related to sound and music, carrying out competitive research at the international level and at the same time transferring its results to society. To that goal the MTG aims at finding a balance between basic and applied research while promoting interdisciplinary approaches that incorporate knowledge and methodologies from both scientific/technological and humanistic/artistic disciplines.
22. Music X Lab | NYU Shanghai
PI: Gus Xia
How can AI help people better compose, perform, and appreciate music while keeping humans in the loop? And more broadly, how could Computer Music make the world more creative, expressive, and interactive while embracing the humanity?
To answer the questions above, we design various algorithms & intelligent systems to understand and extend human musical creativity and expression. To understand means to learn the musical representation conveyed through symbolic notations, performance controls, and acoustic signals. To extend means to use such an understanding to create artificial music partners, serving music lovers at all levels. Some representative works of Music X Lab include interactive composition via style transfer, human-computer interactive performances, autonomous dancing robots, and haptic guidance for flute tutoring.
23. Music and Audio Computing Lab | KAIST
PI: Juhan Nam
Country: South Korea
The Music and Audio Computing Lab (MACLab) is a music research group in the Graduate School of Culture Technology at KAIST. Our mission is to improve ways people enjoy, play, and make music through technology. Specifically, we are working on topics in the following research fields: Music Informational Retrieval, Audio Signal Processing, Machine Learning / Deep Learning for Music and Audio, Computational Modeling of Music Performance, Human-AI Interaction for Music, Sound Synthesis and Digital Audio Effects.
24. Music and Audio Research Group (MARG) | Seoul National University
PI: Kyogu Lee
Country: South Korea
Music and Audio Research Group (MARG) at Seoul National University is a highly interdisciplinary research group that aims to find solutions to a number of intriguing problems in audio/music that still remain unsolved, by using digital audio signal processing and machine learning as two main tools. The best part is, the more we learn about human auditory perception/cognition, the more interesting problems we discover. The research topics covered include, but are not limited to:
- source separation
- speech/singing voice synthesis
- speech/music representation learning
- unsupervised learning for speech/music
- speech/music perception
25. Music and Audio Research Lab (MARL) | New York University
PI: Juan Pablo Bello
Country: United States
MARL brings together scholars from a variety of disciplines to explore the intersection between music, sound, technology, and science. The center currently agglomerates the research activities of over 30 scholars from disciplines such as music technology, theory, composition and education, computer and data science, electrical engineering, psychology, digital media, and urban science. We seek to improve our understanding of the cognitive neuroscience of music and sound; to develop cutting edge methods for acoustic data collection, machine listening, and computational analysis; to advance research in new modes of sound immersion and interaction; and to innovate music creativity and education through technology.
26. Music and Sound Cultures (MaSC) | New York University Abu Dhabi
PI: Carlos Guedes
Country: United Arab Emirates
The Music and Sound Cultures research group (MaSC — https://masc.hosting.nyu.edu) is a collective of researchers focusing on the multidisciplinary study of music from the Gulf, the Levant, East Africa, and South India. These researchers represent a broad spectrum of expertise, including ethnomusicology, machine learning, music composition, performance and improvisation, library science, computational modeling, and the digital humanities. Having as a primary goal the study and dissemination of music from this region, the group currently hosts several projects that range from the preservation of collections of field or rare commercial recordings, to developing innovative ways of conducting musical heritage analysis, preservation, and dissemination. The different projects undertaken by the group ultimately aim at creating new ways of meaningfully interacting with music heritage, allowing scholars to browse large collections of music through their audible or structural characteristics, through the computational recreation of the musical styles using innovative software applications and through the intersection of musical heritage archival materials, including photos and recordings that could be lost to time if not preserved in perpetuity.
27. NUS Sound and Music Computing Lab | National University of Singapore
PI: Ye Wang
The NUS Sound and Music Computing Lab strives to develop Sound and Music Computing (SMC) technologies, in particular Music Information Retrieval (MIR) technologies, with an emphasis on applications in e-Learning (especially computer-assisted music and language edutainment) and e-Health (especially computer-assisted music-enhanced exercise and therapy). Leveraging on neuroscience as a glue, we seek to harness the synergy of SMC, MIR, mobile computing, and cloud computing technologies to promote healthy lifestyles and to facilitate disease prevention, diagnosis, and treatment in both developed countries and resource-poor developing countries. Recently we have also been working on an AI-enabled platform to promote neuroplasticity in personalized language learning for both early and adult language learners.
28. Research about listening lab | James Cook University
PI: Dr Amanda Krause
The Research About Listening lab (http://www.researchaboutlistening.com) aims to produce high quality, innovative social and applied psychology of music research. Current research focuses on examining everyday music experiences, the influence of the arts on health and well-being, and performer and audience interactions in order to advance scientific knowledge and improve individual and community well-being. The lab also seeks to develop early career researchers, providing them with a platform to succeed on their chosen career path, engage with a broad range of stakeholders, and provide leadership within #MusicScience fields.
29. Semantic Music Technologies | Fraunhofer Institute for Digital Media Technology
PI: Hanna Lukashevich
The Fraunhofer Institute for Digital Media Technology IDMT is one of over 70 institutes of the Fraunhofer-Gesellschaft, the world’s leading organization for applied research based in Germany. Fraunhofer IDMT is specifically focused on applied research on AI driven media management and delivery solutions for the media industry. We would like to showcase our research group “Semantic Music Technologies” that covers a wide spectrum of MIR and machine listening tasks. We have a team of about 10 researchers, including PhD and Master students, who conduct applied research in various areas as AI-based music analysis for search, recommendation, game and educational applications, and music production; audio figerprinting; as well as classification of acoustic scenes and detection of audio events.
30. Sound and Music Analysis Group (SoMA) | Birmingham City University
PI: Jason Hockman, Ryan Stables
Country: United Kingdom
The Sound and Music Analysis (SoMA) Group in the Digital Media Technology Laboratory (DMT Lab) provides solutions that expand our understanding of the sonic world. The group has emerged at the intersection of electronic engineering, computer and data science and computational musicology.
Bridging cutting-edge audio signal processing, music information retrieval and machine learning techniques, our research investigates novel interactions with audio documents for the objective of developing frameworks, best practices and tools for analysis and sound and music production.
The outputs of the group are geared towards both analysis and preservation technologies for social and educative benefit and the generation of novel approaches to music creation, equipping future-minded artists with future tools to create future music.
31. Sound Interaction and Computing (SInC) Lab | New Jersey Institute of Technology
PI: Mark Cartwright
Country: United States
The Sound Interaction and Computing (SInC) Lab is headed by Prof. Mark Cartwright in the Informatics Department of New Jersey Institute of Technology. The lab pursues research at the intersection of Human-Computer Interaction and Machine Learning with the aim of building tools to aid in the understanding and the creative expression of sound. To do so, the lab studies people’s needs and practices, researches new technology to meet those needs, and then studies how people use the new technology.
32. UP Digital Signal Processing Laboratory | University of the Philippines (Diliman Campus)
PI: Crisron Rudolf G. Lucas
UP DSL Lab aims to produce excellent, innovative and nationalistic DSP engineers, perform research in areas that are relevant to the Philippine setting, and catalyze product development in DSP applications in the Philippines. Our fields of specialization include Multimedia Processing (Audio, Music and Speech), Machine Learning, and Physical Modeling and Simulations. Currently we have 3 PhD faculty, 5 MS faculty, and 1 ongoing PhD teaching assistant.
33. Utrecht MIR Lab | Utrecht University
PI: Anja Volk and Frans Wiering
We are conducting research at the intersection of computer science and music, connecting computer science methodology to state-of-the-art domain knowledge of music. Our main research areas comprise Music Information Retrieval, Computational/Digital Musicology, Music Technology for Games and Virtual Worlds, and Music Recommendation. Our research has been applied to areas such as melody retrieval; computational music analysis; cultural heritage; big data in music history; fairness in music recommender systems; and music, computing, and health. We collaborate with cultural heritage institutions, such as the Meertens Institute in Amsterdam, with musicologists (e.g. the University of Amsterdam, Goldsmith College), and with music therapists and neuropsychologists (e.g. Leiden Institute for Brain and Cognition, University Medical Center Utrecht).