WiMIR Workshop 2019 Project Guides


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This is a list of Project Guides and their areas of interest for the 2019 WiMIR Workshop, which will take place on Sunday, 3rd November 2019 as a satellite event of ISMIR2019.  These folks will be leading the prototyping and early research investigations at the workshop.

This year’s Workshop is organized by Blair Kaneshiro (Stanford University), Katherine M. Kinnaird (Smith College), Thor Kell (Spotify), and Jordan B. L. Smith (Queen Mary University of London) and is made possible by generous sponsorship from Pandora and Spotify.

Planning to attend the WiMIR Workshop?
Read about the Project Guides and their work in detail below, and sign up to attend at https://forms.gle/mCEod8AvtqnBcMJz7


Amelie Anglade

Ryan Groves

Amélie Anglade and Ryan Groves:  Auto-BeatSaber: Generating New Content for VR Music Games

In this workshop we will dive into a specific problem at the intersection of music, gaming, and dance: the generation of a BeatSaber song level. BeatSaber is one of the most popular VR titles, in which the core of the gameplay is to rhythmically slice incoming boxes with light sabers to the sound of the beat. The game has sparked a huge community of modders who create their own choreographies to existing songs, as well as MIR-based tools (such as a MIDI converter or a BPM estimator) designed specifically to support level creation. The task of this workshop will be: how could we use machine learning to generate these choreographies automatically? Participants will have the opportunity to learn from our experience as Data Science & MIR consultants as we will share our own structured process for problem-solving in the music tech industry.

Dr. Amélie Anglade is a Music Information Retrieval and Data Science consultant. She completed her PhD at Queen Mary University of London, before moving to industry, initially taking on positions in R&D labs such as Sony CSL, Philips Research and CNRS, and then being employed as an MIR expert for Music Tech startups such as SoundCloud and frestyl. For the past 5 years she has further developed her expertise in music identification and discovery–assisting startups and larger companies in the AI and music or multimedia space as an independent consultant, researching, prototyping, and scaling up Machine Learning solutions for them. Additionally, Amélie is a contributor to the EU Commision as an independent technical expert in charge of reviewing proposals and ongoing EU projects. In her spare time she attends music hackathons (15+ so far), and is a teacher and mentor for women in the field of data science through multiple organizations.

Ryan Groves is an award-winning music researcher and veteran developer of intelligent music systems. He received his Master’s in Music Technology from McGill University. In 2016, his work on computational music theory was awarded the Best Paper at ISMIR. He also has extensive experience in industry, building musical products that leverage machine learning. As the former Director of R&D for Zya, he developed a musical messenger app that automatically sings your texts, called Ditty. Ditty won the Best Music App of 2015 by the Appy Awards. More recently, he co-founded Melodrive, where he and his team built the first artificially intelligent composer that could compose music in realtime and react to interactive scenarios such as games and VR experiences. He now works as a consultant and startup advisor in Berlin, with a focus on expanding the use cases of music and audio through the application of AI.

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Ashley Burgoyne: Cognitive MIR with the Eurovision Song Contest

When Duncan Laurence triumphed at the 2019 Eurovision Song Contest in Tel Aviv, it was the Netherlands’ first victory in the contest since 1975 – and perfect timing for the ISMIR conference! One of the most-watched and discussed broadcasts in Europe, data about the Song Contest are an excellent opportunity to link the patterns we can find using MIR tools in audio to real-world human behaviour. This workshop will show you how, and teach you techniques you can use wherever you want to use MIR to understand not just music but also people.

We will consider a number of questions. Every year, the bookmakers try to predict the contest winner: can MIR do better? The same songwriters write the songs for multiple countries each year: is there nonetheless a typical sound for each country’s entry? People assume that voting is politically rather than musically based, but recent research has called those assumptions into question: what does the music tell us? And can we link Eurovision tracks to what fans say on Twitter or direct experimentation about what they hear?

John Ashley Burgoyne is the Lecturer in Computational Musicology at the University of Amsterdam and part of the Music Cognition Group at the Institute for Logic, Language, and Computation. Dr Burgoyne teaches in both musicology and artificial intelligence and is especially interested in musicometrics: developing behavioural and audio models that are conceptually sound, reliable, and musicologically interpretable as music enters the digital humanities era. He was the leader of the Hooked on Music project, an online citizen science experiment to explore long-term musical memory that attracted more than 170,000 participants across more than 200 countries.

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Estefanía Cano and Jakob Abeßer: Learning about Music with MIR

What does John Coltrane have in common with Cannonball Adderley? What makes micro-timing in Brazilian samba unique? What are the tuning characteristics of the harpsichord? Which cues do musicians use to control ensemble intonation? The MIR community has been working for decades in developing reliable methods for research tasks such as beat tracking, melody estimation, chord detection, music tagging, among many others. While most of these methods are not yet perfect, they can certainly be useful tools when attempting to answer questions as the ones above. This holds true especially if computational analysis tools are combined with the experience from musicians, the insights from human listeners, and music knowledge.

This workshop will focus on exploring ways in which we can gain new knowledge about music by combining available MIR techniques and human musical expertise. Instead of focusing on improving MIR methods or in proposing new ways to solve MIR tasks, we want to use this workshop as a platform to brainstorm new questions about the various aspects of music. We want to revisit old questions and propose new alternatives to address them. We want to look back at previous projects and studies, and use the lessons we learned to improve the way we address questions today.

Estefanía Cano is a research scientist at the Semantic Music Technologies group at Fraunhofer IDMT in Germany. Estefanía received her B.Sc. degree in electronic engineering from the Universidad Pontificia Bolivariana, Medellín- Colombia, in 2005, her B.A. degree in Music- Saxophone Performance from Universidad de Antioquia, Medellín-Colombia, in 2007, her M.Sc. degree in music engineering from the University of Miami, Florida, in 2009, and her Ph.D. degree in media technology from the Ilmenau University of Technology, Germany, in 2014. In 2009, she joined the Semantic Music Technologies group at the Fraunhofer Institute for Digital Media Technology IDMT as a research scientist. In 2018, she joined the Social and Cognitive Computing Department at the Agency for Science, Technology and Research A*STAR in Singapore. Her research interests include sound source separation, music education, and computational musicology.

Jakob Abeßer studied computer engineering (Dipl.-Ing., 2008) and media technology (Dr.-Ing., 2014) at the Ilmenau University of Technology. Since 2008, he has been working in the field of semantic music processing at the Fraunhofer Institute for Digital Media Technologies (IDMT) in Ilmenau. In 2005 and 2010 he spent 2 stays abroad at the Université Paul Verlain in Metz, France and the Finnish Centre of Excellence in Interdisciplinary Music Research at the University of Jyväskylä in Finland. Between 2012 and 2017, he also worked as a doctoral researcher in the Jazzomat Research Project at the Franz Liszt School of Music in Weimar, developing methods for the computer- aided analysis of jazz improvisations. Since 2018 he is working as co-investigator of the research project “Informed Sound Activity Detection in Music Recordings” (ISAD) at Fraunhofer IDMT in collaboration with Prof. Dr. Meinard Müller from the International Audio Laboratories in Erlangen, Germany. His current research interests include music information retrieval, machine listening, music education, machine learning and deep learning.

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Matthew Davies and Sebastian Böck:  Building and Evaluating a Musical Audio Beat Tracking System

The task of musical audio beat tracking can be considered one the foundational problems in the music information retrieval community. In this workshop we seek to take a tour of the entire beat tracking pipeline by addressing the following steps: i) how to manually annotate ground truth ii) how to construct a lightweight beat tracking model using deep neural networks; iii) how to select appropriate musical material for training and testing; and iv) how to conduct evaluation in a musically meaningful way. In each of these areas, we seek to provide practical hands-on experience and acquired tacit knowledge concerning what works and also what doesn’t work. Throughout the workshop we will promote active participation and discussion with the aim of driving new research in beat tracking and fostering new collaborations.

Matthew Davies is a music information retrieval researcher with a background in digital signal processing. His main research interests include the analysis of rhythm in musical audio signals, evaluation methodology, creative music applications, and reproducible research. Since 2014, Matthew has coordinated the Sound and Music Computing Group in the Centre for Telecommunications and Multimedia at INESC TEC. From 2014-2018, he was an Associate Editor for the IEEE/ACM Transactions on Audio, Speech and Language Processing and coordinated the 4th Annual IEEE Signal Processing Cup. He was a keynote speaker at the 16th Rhythm Production and Perception Workshop, and General Chair of the 13th International Symposium on Computer Music Multidisciplinary Research.

Sebastian Böck received his diploma degree in electrical engineering from the Technical University in Munich in 2010 and his PhD in computer science from the Johannes Kepler University Linz in 2016. Within the MIR community he is probably best known for his machine learning-based algorithms, which pushed the performance of automatic beat tracking and other tasks into regions formerly only achievable by humans. Currently he is continuing his research at the Austrian Research Institute for Artificial Intelligence (OFAI) and the Technical University of Vienna.

Georgi Dzhambazov

Georgi Dzhambazov: Verse and Chorus Detection of Acoustic Cover Versions

Many MIR tasks have as a prerequisite the annotation of structural segments of a song. While cover versions usually retain most music aspects of the original song, there could be a completely new structure (sections appended/missing). In particular, covers with acoustic instrumental accompaniment are characterized by a predominant vocal line, whereby the accompaniment is occasionally missing or improvised. Therefore structure detection algorithms based solely on harmonic features are most likely not a sufficient solution.

In this hands-on-workshop, we will explore the problem of automatic segmentation and labeling of the verse and chorus sections for a given acoustic cover version. Information about the original song (lyrics, chords, guitar tabs etc.) can be found online. Our goal is to come up with ideas/prototypes on how to approach the problem combining existing methods (e.g. vocal activity detection, chord recognition, lyrics-alignment) in new ways, rather than design something completely new. An industry database of acoustic cover songs with a varying degree of modifications to the original structure will be provided.

Georgi holds a PhD on Music Information Retrieval from the Music Technology Group in Barcelona under the supervision of Xavier Serra.  He worked on the topic of automatic alignment of lyrics. He has also experience in applied research on speech recognition and natural language processing. In 2017 he founded VoiceMagix – a company providing solutions for automatic analysis of singing voice.

For several years he is a WiMIR mentor and MIREX task captain.  His research interests are algorithms for the singing voice and speech and machine learning in general. He is currently mainly interested in initiatives aiming at bridging the gap between research in MIR and the music industry.

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Brian McFee:  Coping with Bias in Audio Embeddings

An appealing general approach to modeling problems across many domains is to first transform raw input data through an embedding function, which has been trained on a large (but potentially unrelated) collection of data. This results in a vector representation of each object, which can then be used as input to a simple classifier (e.g., a linear model) to solve some downstream task using a limited amount of data. This approach has been successfully demonstrated in image and video analysis, natural language processing, and is becoming increasingly popular in audio and musical content analysis. However, general-purpose embedding models have been known to encode and propagate implicit biases, which can have detrimental and disparate population-dependent effects.

In this project, we will conduct a preliminary study of embedding bias in MIR data. Using pre-trained audio embeddings and well-known MIR datasets, we will first attempt to quantify the extent to which embedding-based classification exhibits biased results across data sets and/or genres. We will then attempt to de-bias the embedding by adapting recently proposed methods from the natural language processing literature.

Brian McFee is Assistant Professor of Music Technology and Data Science New York University. He received the B.S. degree (2003) in Computer Science from the University of California, Santa Cruz, and M.S. (2008) and Ph.D. (2012) degrees in Computer Science and Engineering from the University of California, San Diego. His work lies at the intersection of machine learning and audio analysis. He is an active open source software developer, and the principal maintainer of the librosa package for audio analysis.


Peter Sobot:  Software Engineering for Machine Learners (and Drummers) – Building Robust Applications with Audio Data  

Building machine learning systems is hard, but building systems that can scale can be even harder. In this workshop, we’ll discuss software engineering techniques to use when building machine learning systems, including methods to make your code easier to write, test, debug, and maintain. We’ll also build an audio sample classifier with these techniques using basic machine learning concepts, and discuss methods for deploying this system at scale. Finally, we’ll take the system to an extreme and use cloud computing to build a system that learns in response to user input.

Peter Sobot is a Staff Engineer at Spotify, where he works on recommendation products at massive scale, including the systems that power Discover Weekly.  His open-source software contributions range from low-level data tools to legendary internet-scale hacks like The Wub MachineHe has spoken at !!Con, Google Cloud Next, and Google Summit, and makes electronic music in his spare time.

Bob Sturm

Bob Sturm: Computer-Guided Analysis of Computer-Generated Music Corpora

Various iterations of the folkrnn system (folkrnn.org) have generated over 100,000 transcriptions of “machine folk”, e.g., https://highnoongmt.wordpress.com/2018/01/05/volumes-1-20-of-folk-rnn-v1-transcriptions/, but manually looking through these takes a lot of time. In this project we will think about and implement some methods that can help one grasp characteristics of such collections, and find interesting bits. For instance, we can look for instances of plagiarism, find anomalous material, judge similarity in terms of pitches, meter, melodic contour, etc.

Bob L. Sturm is currently an Associate Professor in the Speech, Music and Hearing Division of the School of Electronic Engineering and Computer Science at the Royal Institute of Technology KTH, Sweden. Before that he was a Lecturer in Digital Media at the Centre for Digital Music, School of Electronic Engineering and Computer Science, Queen Mary University of London. His research interests include digital signal processing for sound and music signals, machine listening, evaluation, and algorithmic composition. He is also a musician.

Chris Tralie

Chris Tralie: To What Extent Do Cyclic Inconsistencies Exist in Musical Preferences?

Music recommendation algorithms seek to rank a set of candidate songs in order of some estimated user preference.  It may be challenging to ascertain preferences from surveys, however, since Miller’s empirical “rule of 7” could be interpreted to suggest that humans lack the working memory to meaningfully rank much more than 7 items at a time.  To learn a longer list of preferences, then, one could consider presenting only a pair of alternatives at a time and aggregating these pairwise preferences into a global ranking. However, real pairwise rankings can lead to cyclic inconsistencies; that is, people often express that A > B and B > C, but also that C > A.  This is known as the “Condorcet Paradox.” Fortunately, there exists a topological pairwise rank aggregation technique, known as “HodgeRank,”[1] which can aggregate these rankings into the “most consistent” global order, while simultaneously quantifying the degree to which local (A > B > C > A) and global (A > B > C > … > A) exist.  In this workshop, we will first discuss these concepts in more detail, and then we will each listen to pairs of 15 second clips from a diverse corpus of music [2] and rank our preferences, and then apply HodgeRank to see how consistent we all are. We will also use metrics between rankings to show which people in our group have similar preferences.  Zooming out, we will also discuss some social psychology literature that correlates musical preferences to personality traits in music [2], and we will discuss concurrent ethical pitfalls that can emerge when collecting data and interpreting results in such studies.

[1] http://www.ams.org/publicoutreach/feature-column/fc-2012-12

[2] Rentfrow, Peter J., et al. “The song remains the same: A replication and extension of the MUSIC model.” Music Perception: An Interdisciplinary Journal 30.2 (2012): 161-185.

Christopher J. Tralie is a data science researcher working in applied geometry/topology and geometric signal processing. His work spans shape-based music structure analysis and cover song identification, video analysis, multimodal time series analysis, and geometry-aided data visualization. He received a B.S.E. from Princeton University 2011, a master’s at Duke University in 2013, and a Ph.D. in at Duke University in 2017, all in Electrical Engineering. His Ph.D. was primarily supported by an NSF Graduate Fellowship, and his dissertation is entitled “Geometric Multimedia Time Series.”  He then did a postdoc at Duke University in Mathematics and a postdoc at Johns Hopkins University in Complex Systems. He was awarded a Bass Instructional Teaching fellowship at Duke University, and he maintains an active interest in pedagogy and outreach, including longitudinal mentoring of underprivileged youths in STEAM education. He is currently a tenure track assistant professor at Ursinus College in the department of Mathematics and Computer Science. For more info, please visit http://www.ctralie.com.

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TJ Tsai: Generating Music by Superimposing and Adapting Existing Audio Tracks

There has been a lot of work in training models to generate novel music from scratch.  In this workshop, we will explore the possibility of generating music by taking a source audio track and enhancing it by superimposing other segments of existing audio material.  The specific task we will work on is to take a classical piano recording and to overlay techno beats/music in an aesthetically pleasing manner. We will brainstorm different ways to accomplish this task, develop some prototypes, and hopefully generate some new music by the end of the workshop!

Prof. TJ Tsai completed bachelor’s and master’s degrees in electrical engineering at Stanford University.  During college, he studied classical piano with George Barth and participated in the Stanford Jazz Orchestra and the chamber music program.  After graduating, he worked at SoundHound for a few years, and then went to UC Berkeley for his Ph.D. Since 2016 he has been a faculty member in the engineering department at Harvey Mudd College, a STEM-focused liberal arts college in Claremont, CA.

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Gissel Velarde and Andre Holzapfel:  Music Research for Good

In recent years, important advances in artificial intelligence (AI) led to different initiatives considering super-intelligence, its advantages, and dangers. The initiatives fostering beneficial AI include (i) conferences: the AI for Good Global Summit (running since 2017), The Beneficial artificial general intelligence conference (held in 2015, 2017 and 2019), (Iii) the establishment of organizations and projects like OpenAI, Partnership on AI, Google’s AI for Social good, or AI for Humanity from the Université de Montréal, and (iv) governments AI strategies. In last year’s ISMIR conference, our community dedicated a session to discuss ethics in MIR, and there are topics which are still to be addressed. During this workshop, we will review the state of AI for good. We will revisit the tentative ethical guidelines for MIR developers proposed by Holzapfel et. al (2018) and its alignment with guidelines from the IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems (Chatila & Havens, 2019), and Ethics Guidelines for Trustworthy AI from the High-Level Expert Group on Artificial Intelligence [HEGAI] (2019).  We will use tools such as SWOAT (strengths, weaknesses, opportunities, and threats) analysis, business canvas, SCAMPER technique for creative thinking and Gantt charts. After a situational analysis, we will define goals, scope, stakeholders, risks, benefits, impact, and an action plan. Finally, we will elaborate ethics guidelines in music research to be proposed to our community for consideration.

Chatila, R., & Havens, J. C. (2019). The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems. In Robotics and Well-Being (pp. 11-16). Springer, Cham.

High-Level Expert Group on Artificial Intelligence (2019). Ethics guidelines for trustworthy AI. Retrieved from https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai

Holzapfel, A., Sturm, B.L. and Coeckelbergh, M., 2018. Ethical Dimensions of Music Information Retrieval Technology. Transactions of the International Society for Music Information Retrieval, 1(1), pp.44–55. DOI: http://doi.org/10.5334/tismir.13

Gissel Velarde is a computer scientist, engineer, pianist and composer. She holds a PhD degree from Aalborg University for her doctoral thesis “Convolutional methods for music analysis” supervised by David Meredith and Tillman Weyde. She participated as a research member of the European project, “Learning to Create” (Lrn2Cre8), a collaborative project within the Future and Emerging Technologies (FET) programme of the Seventh Framework Programme for Research of the European Commission. She was a machine learning lead at Moodagent and worked as a consultant for SONY Computer Science Laboratories.  She is a DAAD alumni and mentor of the WIMIR program.

Andre Holzapfel received M.Sc. and Ph.D. degrees in computer science from the University of Crete, Greece, and a second Ph.D. degree in music from the Centre of Advanced Music Studies (MIAM) in Istanbul, Turkey. He worked at several leading institutes in computer engineering as postdoctoral researcher, with a focus on rhythm analysis in music information retrieval. His field work in ethnomusicology was mainly conducted in Greece, with Cretan dance being the subject of his second dissertation. In 2016, he became Assistant Professor in Media Technology at the KTH Royal Institute of Technology in Stockholm, Sweden. Since then, his research subjects incorporate the computational analysis of human rhythmic behavior by means of sensor technology, and the investigation of ethical aspects of computational approaches to music.

Eva Zangerle

Eva Zangerle – Multi-Dimensional User Models for MIR

In music information retrieval scenarios (particularly, when it comes to personalization), users and their preferences are often modeled solely by their direct interactions with the system (e.g., songs listened to). However, a user’s perception and liking of recommended/retrieved tracks is dependent on a number of dimensions, which may include the (situational) context of the user (e.g., time, location or activity), the user’s intent, content descriptors and characteristics of tracks the users have listened to and also should be able to model the change of preference over time (short vs. long-term). Comprehensive models that capture these multiple dimensions, however, are hardly devised.

In the scope of this workshop, we aim to look into how users and their preferences (long- and short-term) can be modeled, the implications of such comprehensive user models on the underlying MIR algorithms and also, how we can evaluate the contribution and impact of such user models.

Eva Zangerle is a postdoctoral researcher at the University of Innsbruck at the research group for Databases and Information Systems (Department of Computer Science). She earned her master’s degree in Computer Science at the University of Innsbruck and subsequently pursued her Ph.D. from the University of Innsbruck in the field of recommender systems for collaborative social media platforms. Her main research interests are within the fields of social media analysis, recommender systems, and information retrieval. Over the last years, she has combined these three fields of research and investigated context-aware music recommender systems based on data retrieved from social media platforms aiming to exploit new sources of information for recommender systems. She was awarded a Postdoctoral Fellowship for Overseas Researchers from the Japan Society for the Promotion of Science allowing her to make a short-term research stay at the Ritsumeikan University in Kyoto.


WiMIR Workshop 2018: Building Collaborations Among Artists, Coders and Machine Learning

We’re very pleased to link to this very fine blog post by Matt McCallum, Amy Hung, Karin Dressler, Gabriel Meseguer-Brocal, and Benedikte Wallace about their group work at the 2018 WiMIR Workshop!

WiMIR Workshop 2018: Modeling Repetition and Variation for MIR

Blog post by Iris Yuping Ren, Hendrik Vincent Koops, and Anja Volk.

(Materials are available at https://github.com/hvkoops/wimir2018)

Right after the main ISMIR2018 conference, the WiMIR workshop awaited. As planned, we gathered and formed a working group to tackle the problem of modeling repetition and variation in music for MIR, consisting of the following participants:

Anja Volk (Utrecht University) – Project Guide
Hendrik Vincent Koops (Utrecht University) – Project Guide
Iris Yuping Ren (Utrecht University) – Project Guide
Juan Pablo Bello (New York University)
Eric Nichols (Microsoft)
Jaehun Kim (Delft University)
Marcelo Rodriguez Lopez (Yousician)
Changhong Wang (Queen Mary University of London)
Jing Chen (Nanchang University)
Tejaswinee Kelkar (University of Oslo)

In the morning session, we first reflected on the background of repetitions and variations as central concepts in music observed by musicologists, and then the computational modeling thereof in Music Information Retrieval within different contexts. We discussed that there exists disagreement in annotations in many MIR tasks, such as automatic chord extraction and repeated pattern discovery. Comparable to many other subareas in machine learning and data science, we face complications brought by the unattainability of an absolute, all-encompassing ground truth annotation.

We then provided more detailed motivations and ideas on how to gather annotations on repetitions and variations in music. For example, one set of guidelines was:

Listen to the following pieces and annotate the salient melodic patterns with

  1. How relevant this pattern is to this piece
  2. One word to label the type of this pattern
  3. A short description on why you find it to be a pattern
  4. How difficult it was for you to decide whether it’s a pattern

Using the prepared materials, we had a very active discussion on topics such as: how to define the concepts for specific annotation tasks? How can we use tools such as wearable sensors, a wrist band for example, to help the annotation process?  How can we compare the annotations and annotation methods? (For more, please refer to the github link).

In the afternoon, after a very interesting and useful lunch breakout session, we started with the actual annotation process on the first page of the String Quartet No. 1 in F major, Op. 18, No. 1, Ludwig van Beethoven (1798 and 1800), Violin I. We provided the sheet music, midi and audio files. The participants used different tools to their liking to mark the repetitions and variations on the sheet music. During the annotations, there were already some interesting discussions in some subgroups: how repetitive the young Beethoven was!

In the second part of the afternoon, using the individual annotations, we began our exchange on the experience of the annotation process. We discussed how we can improve on the current designs of annotation processes and tools for annotation tasks, and how the annotated patterns could be used to design an automatic pattern discovery system. We concluded the day with a short presentation.

Throughout the day, we gained many new insights into what are the good and bad ways to create and employ annotations on repetitions and variations. We warmly thank the participants for a great a day of discussions, listening to music, and annotating repetitions and variations!

Iris Yuping Ren is a second year PhD candidate in the Information and Computing Sciences department, Utrecht University, under the supervision of dr. Anja Volk, dr. Wouter Swierstra and dr. Remco C. Veltkamp. She obtained Bachelor degrees in Statistics and Cultural Industry Management from Shandong University, Master degrees in Complex System Science from the University of Warwick and École Polytechnique, Computer and Electrical Engineering in the University of Rochester, and a diploma in violin performance from the Eastman Community Music School. Her current research has a focus on the computational modelling and statistical analysis of musical patterns in various corpora. She is comparing both generic and domain-specific approaches, such as data mining methods, times series analysis, machine learning based clustering and classification algorithms. To discover useful patterns in music, she makes use of functional programming languages to compute pattern transformations and similarity dimensions. Her research contributes to a computationally- and quantitatively-based understanding of music and algorithms. Music wise, she enjoys playing with local orchestra projects and sessions.

Hendrik Vincent Koops is a PhD candidate at Utrecht University under supervision of Dr. Anja Volk and Dr. Remco C. Veltkamp. Vincent holds degrees in Sound Design and Music Composition from the HKU University of the Arts Utrecht, and degrees in Artificial Intelligence from the Utrecht University. After a research internship at Carnegie Mellon University, he started his PhD in Music Information Retrieval. His PhD research concerns the computational modeling of variance in musical harmony. For example, he studied annotator subjectivity to better understand the amount of agreement we can expect among harmony annotators. Using data fusion methods, he investigated how to integrate multiple harmony annotations into a single, improved annotation. For a deep learning study, he created new features for chord-label personalization. Vincent’s research contributes to a better understanding of computational harmony analysis tasks, such as automatic chord estimation. Vincent is also active as a composer for film and small ensembles. Currently, he’s working towards work for a string quartet.

Anja Volk (Utrecht University), holds master degrees in both mathematics and musicology, and a PhD in the field of computational musicology. The results of her research have substantially contributed to areas such as music information retrieval, computational musicology,  music cognition, and mathematical music theory.  In 2016 she launched together with Amélie Anglade, Emilia Gómez and Blair Kaneshiro the Women in MIR (WIMIR) Mentoring Program.  She co-organized the launch of the Transactions of the International Society for Music Information Retrieval, the open access journal of the ISMIR society, and is serving as Editor-in-Chief for the journal’s first term. Anja received the Westerdijk Award 2018 from Utrecht University in recognition of her efforts on increasing diversity.

Start of Peer Mentoring Sign up

For all mentors of the WiMIR mentoring round 2019, we offer Peer Mentoring. In this blog post we would like to familiarize you with the Peer Mentoring sign-up procedure. This year we use Trello (explanations follow below), please note that the sign-up deadline is March 10th.

What is peer mentoring?

Peer mentoring provides mentors with the opportunity to discuss various career aspects with other mentors in the WiMIR program. Peer mentoring is meant to serve as a complement to the traditional mentoring program, in which mentor and mentee in any given pair typically have an unequal amount of experience in the field of MIR. In the case of peer mentoring, the two peers may have comparable amounts of experience, yet benefit from each other by bringing in various perspectives on MIR. For example, these perspectives may differ in terms of scholarly background, geographical affiliation, working environment, MIR subfield of expertise, experience with teaching and public outreach, technical skill set, mentoring practices, and more.

Our hope is that the peer mentoring program will contribute to reinforcing the cohesion of the MIR community at large, and in particular: across countries and continents, across scientific disciplines, and between academia and industry. Furthermore, we aim to frame this program within the core mission of WiMIR, that is, to increase the opportunities of women in the field of MIR. Therefore, we encourage WiMIR mentors of all genders to take part in the peer mentoring program, and adopt this communication channel as a facilitator of diversity and inclusion.

How does peer mentoring work?

The first stage of the peer mentoring program is for you to introduce yourself to the rest of the WiMIR mentors. The second stage is to read the profiles of other participants, rank them by order of preference, and send us the ranked list of your top choices. The third stage, once you are assigned a peer, is for you to connect with them through private electronic communication.

Why use Trello?

Last year, the interface for introducing oneself to other peer mentors, and selecting a peer, was a simple shared spreadsheet on Google Documents. This year, because of the rising number of participants, we have decided to migrate to another interface: Trello. This might seem like an iconoclastic choice given that, for those of you who already know Trello, it is primarily designed to be a tool for task management rather than team building. Yet, as it turns out, the streamlined drag-and-drop interface of a Trello board is actually perfect for us to collect the ranked list of preferences of each participant.

A Trello board consists of items (“cards”) which can be moved from one column (“list”) to another. In project management, cards are tasks and lists are states of completion. However, in our peer mentoring interface, we will be using cards to denote participants, and lists to denote preference. For the time being, there is a single Trello board, and it is only visible to us organizers. We kindly ask participants to fill in this Trello board via an email interface.

In an upcoming stage, we will duplicate this Trello board and send a different, private copy to every one of you. At that point, your role will be to browse through the Trello cards of other participants and rank the ones you want to meet.

How do I introduce myself to other mentors?

You can introduce yourself by creating your Trello card. Rather than giving global access to the entire Trello board, we propose that you use the email-to-board interface of Trello. This interface is lighter, more portable, and more accessible to people with disabilities than the visual interface.

Here is a link describing the email-to-board interface of Trello: https://help.trello.com/article/809-creating-cards-by-email

We particularly point your attention towards the “formatting tips” paragraph.

The subject of the email you will send will become the title of the Trello card.

For consistency, we ask everyone to title their Trello card with three elements, separated by spaces:

  1. The two-letter abbreviation of the country of affiliation
  2. Your full name.
  3. Some hashtags describing your own interests in MIR.

For example:

  • “US Vincent Lostanlen #academia #symbolic #timbre”
  • “NL Vincent Koops #academia #harmony #rhythm”
  • “US Blair Kaneshiro #industry #cognition #performance”
  • etc.

The list of recommended hashtags includes, but is not limited to:

  • #academia
  • #accessibility
  • #behavior
  • #business
  • #careers
  • #cognition
  • #corpora
  • #creation
  • #dance
  • #diversity
  • #ethics
  • #health
  • #indexing
  • #industry
  • #melody
  • #metadata
  • #methodologies
  • #performance
  • #recommendation
  • #rhythm
  • #semantic
  • #structure
  • #symbolic
  • #style
  • #teaching
  • #transcription
  • #timbre
  • #voice

You may include other hashtags in the email subject as you see fit. We highly recommend using the #academia and #industry hashtags, and at least two others in the list.

What to put on my Trello card?

In the email body, we ask mentors to include a small profile to their card that includes some biographical information, research interests, and reasons for wanting to participate in peer mentoring. A recommended outline is

  • Some biographical information
  • Current position(s)
  • Research interests and goals
  • Current research focus
  • Interests and pursuits outside of research
  • Projects you are currently working on
  • Your goals in peer mentoring 

As an example, below is the information on the card of Vincent Lostanlen.

– He/him. 26 years old
– a postdoc at NYU’s Music and Audio Research Lab
– visiting scholar from the Cornell Lab of Ornithology, working on bioacoustics
– research goal: MIR applications at the interaction between signal processing and deep learning
– current focus: contemporary music techniques, timbral and structural similarity
– outside of research: computer music designer for Florian Hecker
– open source: Kymatio, librosa, scattering.m

In addition to scientific topics, I would like to progress in my understanding of diversity and inclusion in MIR, ethical responsibility, and fostering links between research and creation.

I would prefer to establish contacts with peers in France or the UK.

Background: EngD Télécom Paristech 2013, MSc Ircam 2013, PhD applied math ENS 2017.

You can also write in prose if you prefer. Feel free to add as many details about yourself as you want.

What is the email address I should write to?


Again, please make sure that the subject of the email contains your country of affiliation, your full name, and some hashtags of interest.

Deadline: Please make sure you email your Trello card by March 10th.

What if I want to connect with multiple mentors at once?

Although the peer mentoring program is designed to focus on one-to-one communication, we wish to point out that there is also a recommended communication channel for broadcasting messages to all mentors of the WiMIR program. This communication channel is the channel #wimir_mentors_ on the “MIR community” Slack workspace. Below is the link:


Please note, however, that Slack restricts the history of this workspace to the most recent 10,000 messages. Thus, even though this channel is OK for short-lived announcements, it is not ideal for keeping track of the evolution of long-term projects.

Who are the Peer Mentoring Coordinators?

The Peer Mentoring Coordinators will help you find a suitable peer mentor: Hendrik Vincent Koops (Utrecht University) and Vincent Lostanlen (New York University). If you have questions, please contact them at the following email addresses: h.v.koops@gmail.com and vincent.lostanlen@nyu.edu

TL;DR if you are a WiMIR mentor in the 2019 round and want to join the peer mentoring program, please write an email to wimirpeermentors+ardgsv5nleozfdsnxnbx@boards.trello.com

with an email subject of the form “US Vincent Lostanlen #academia #symbolic #timbre” and personal information about yourself in the email body.

With our best wishes,

The Peer Mentoring Coordinators

Hendrik Vincent Koops and Vincent Lostanlen

WiMIR mentoring round 2019 kickoff

The fourth round of the WiMIR mentoring program is about to start,  mentors and mentees have been matched and introduced  to each other by the Mentoring Program Committee.  Participants come from Europe, North and South America,  Oceania and for the first time from Africa. Thanks everyone for contributing and keeping your commitment! Happy mentoring!

WiMIR mentoring 2019 participants

Mentoring Program Committee

  • Johanna Devaney, Brooklyn College, US
  • Ryan Groves, Melodrive, Germany
  • Blair Kaneshiro, Stanford University, US
  • Anja Volk, Utrecht University, the Netherlands

Peer Mentoring Coordinators

  • Hendrik Vincent Koops, Utrecht University, the Netherlands
  • Vincent Lostanlen, New York University, US

Our mentees reside in Australia, Austria, Belgium, Brazil, Denmark, France, Germany, Greece, India, Ireland, Italy, Netherlands, Nigeria, Norway, Singapore, South Korea, Spain, Taiwan, Turkey, United Kingdom, and United States. They represent a diverse field of interests and backgrounds, such as artificial intelligence, signal processing, natural language processing, musicology, applied mathematics, computer science, audio signal processing, psychoacoustics, human computer interactive performance, computational musicology, music perception and cognition, data science, complex system, acoustics, physics, machine learning, software engineering, ethnomusicology, composition, music therapy, neuroscience, and psychology.

We thank our generous mentors from Europe, North and South America, Asia and Oceania for dedicating their time to this program:

Kat Agres, IHPC (A*STAR), Singapore
Steinunn Arnardottir, Native Instruments GmbH, Germany
Thomas Arvanitidis, MUSIC Tribe, United Kingdom
Andreas Arzt, Johannes Kepler University, Austria
Ana M. Barbancho, Universidad de Málaga, Spain
Isabel Barbancho, Universidad de Malaga, Spain
Dogac Basaran, IRCAM, France
Christine Bauer, Johannes Kepler University Linz, Austria
Amy Beeston, University of Leeds, UK (Scotland)
Brian Bemman, Aalborg University, Denmark
Francesco Bigoni, Aalborg University – Copenhagen, Denmark
Rachel Bittner, Spotify, United States
Tom Butcher, Microsoft, USA
Marcelo Caetano, Freelance, Argentina
Mark Cartwright, Apple, UK
Doga Cavdir, CCRMA, Stanford University, United States
JOe Cheri Ross, Linkedin, India
Srikanth Cherla, Jukedeck, Sweden
Orchisama Das, Stanford University (CCRMA), United States
Matthew Davies, INESC TEC, Portugal
Andrew Demetriou, TU Delft, Netherlands
Chris Donahue, UC San Diego, US
Jonathan Driedger, Chordify, Germany/The Netherlands
Andrew Elmsley, Melodrive, Germany
Philippe Esling, IRCAM – Sorbonnes Universités, France
Sebastian Ewert, Waikato University, New Zealand
Ichiro Fujinaga, McGill University, Canada
Fabien Gouyon, Pandora, UK/Portugal
Ryan Groves, Melodrive Inc., Germany
Blair Kaneshiro, Stanford University, USA
Thor Kell, Spotify, United States
Peter Knees, TU Wien, Austria
Hendrik Vincent Koops, Utrecht University, Netherlands
Katerina Kosta, Jukedeck, United Kingdom
Nadine Kroher, MXX Music, Spain
Robin Laney, Open University, UK
Audrey Laplante, Université de Montréal, Canada
Alexander Lerch, Georgia Institute of Technology, USA
Mark Levy, New York University, United States
Michael Mandel, Brooklyn College, CUNY, USA
Ethan Manilow, Northwestern University, USA
Matthew McCallum, Gracenote, United States
Brian McFee, New York University, United States
Blai Meléndez-Catalán, UPF / BMAT, Spain
Gabriel Meseguer Brocal, Ircam, France
Meinard Mueller, International Audio Laboratories Erlangen, Germany
Néstor Nápoles López, McGill University, Canada
Eric Nichols, Microsoft, USA
Oriol Nieto, Pandora, USA
Sergio Oramas, Spotify, United Kingdom
Ritu Patil, Cummins college of engineering, India
Johan Pauwels, Queen Mary University of London, United Kingdom
Marcelo Queiroz, University of São Paulo, Brazil
Elio Quinton, Universal Music Group, United Kingdom
Colin Raffel, Google Brain, USA
Preeti Rao, IIT Bombay, India
Christopher Raphael, Indiana Univ., USA
Justin Salamon, New York University, USA
Andy Sarroff, iZotope, USA
Bertrand Scherrer, LANDR AUDIO INC., Canada
Sertan Şentürk, Pandora, Spain
Amina Shabbeer, Amazon, United States
Ajeet Singh, India
Joren Six, IPEM, Ghent University, Belgium
Jordan Smith, United Kingdom
Mohamed Sordo, Pandora, United States
Ajay Srinivasamurthy, Amazon Alexa, India, India
Bob Sturm, KTH, Sweden
Derek Tingle, IDAGIO, Germany
Christopher Tralie, Duke University, United States
Marcelo Tuller, INESC TEC, Portugal
Doug Turnbull, Ithaca College, United States
Makarand Velankar, MKSSS’S Cummins College of Engineering, Pune, India
Gissel Velarde, Moodagent, Denmark
Christof Weiss, International Audio Laboratories Erlangen, Germany
Chih-Wei Wu, Netflix, Inc., U.S.
Gus Xia, NYU Shanghai, USA/China

Sign-ups open for WiMIR mentoring round 2019

For preparing the fourth round of the Women in Music Information Retrieval (WiMIR) mentoring program, to begin in January 2019,  we kindly invite previous and new mentors and mentees to sign up  through the following signup forms:

Sign up to GET a mentor in 2019 here: http://bit.ly/2Ns8ulj

Sign up to BE a mentor in 2019 here: http://bit.ly/2Da6ZTZ

Signups close Nov 30, 2018. Mentor/mentee matches will be announced in January 2019.

The WiMIR mentoring program connects women students, postdocs, early-stage researchers, industry employees, and faculty to more senior women and men in MIR who are dedicated to increasing opportunities for women in the field. Mentors will share their experiences and offer guidance to support mentees in achieving and exceeding their goals and aspirations. The program offers to all mentors the option to pair up with a peer mentor for discussing relevant topics with a professional at a similar stage of their career.  By connecting individuals of different backgrounds and expertise, this program strengthens networks within the MIR community, both in academia and industry. 

Time commitment: four remote meetings between January and end of June 2019.

Who is eligible?

– Female undergraduates, graduate students, postdocs, early-stage researchers, industry employees, and faculty may sign up as mentees. 

– Graduate students, industry employees, researchers, and faculty of any gender may sign up as mentors. 

– Those meeting criteria for both mentor and mentee roles are welcome to sign up as both. 

Faculty: Please share this announcement with female undergraduates in your departments and labs who may be interested in participating. The mentoring program can help attracting newcomers at an early stage to the MIR field.

More information on the program

General information: https://wimir.wordpress.com/mentoring-program/ 

Report on the mentoring round in 2017:  http://bit.ly/2yuWS5i

Report on the mentoring round in 2018:  https://bit.ly/2P5pBG3

Questions? Email wimir-mentoring@ismir.net 

We look forward to your response and commitment to continuing the mentoring program!

The WiMIR Mentoring Program Committee

Johanna Devaney, Ryan Groves, Blair Kaneshiro, and Anja Volk

WiMIR Mentoring Program Report 2018: On the “only meeting that should last longer”


Poster design: Julia Wilkins

Blog post by Anja Volk (Utrecht University), Co-Founder of the WiMIR Mentoring Program

“This is the only hour-long meeting on my calendar that I secretly wish would last longer.” Let’s take this quote from a mentor’s anonymous feedback on his/her experience with the WiMIR mentoring program as the opening fanfare to our report on the outcomes of the 2018 mentoring round as reflected by the participants. I can hardly think of any bigger compliment to this program from the perspective of a busy mentor. Before looking into what other mentors and mentees told us in their anonymous feedback about their experience with the program, allow me some remarks on reports in the field of Music Information Retrieval.

We love big numbers in Music Information Retrieval – we are fans of analyzing millions of musical pieces and reporting statistics. Accordingly, our report on the WiMIR mentoring round in 2018 might deal with a lot of numbers, such as the fact that the number of participants has doubled again as in previous years, with 80 mentor-mentee pairs enrolling this time, while participants came from Europe, North and South America, Asia and Oceania. Or we might count how the list of academic institutions participating has only grown since the first round in 2016, with about 70 institutions participating in 2018,  and that we have meanwhile mentors from most of the leading music technology companies and even AI and music startups, adding up to about 40 companies. You can check that out here.

However, let’s take in this report the musicologist’s approach of giving great care to details, analyzing one piece after the other (and not necessarily millions at once) and let’s listen to one piece at a time, or better to one story at a time on how the mentees felt empowered, gained career perspectives, came to appreciate the MIR community and felt encouraged and included through the mentoring sessions. These individual stories might give a more detailed picture on what has been gained than plain numbers.

For a description of the general format of the WiMIR mentoring program, with 4 remote meetings between mentor and mentee, you can check out last year’s report here

Exposition first theme

Outcomes from mentoring sessions on career perspectives as reported by mentees

The following anonymous quotes provide an overview of how mentees were able to gain clarities and perspectives on their career options in MIR.

Not only has the WiMIR mentoring programme opened up opportunities for me to study it and work abroad, experiencing other universities, it has built my confidence with networking with more senior academics.

I already told this to anyone I met working in wide ranged related fields. This is the best way to find your path to learn or make career goals.

With his guidance, I found my path through my best interests in both academic and industrial ways.

I have more clarity on my career path.

Discussing with a successful woman in this field was very interesting so that I could ask specific questions about my work/life path that would help me making decisions for my future career.

It deepened my understanding on research from the perspective of a big picture.

The program provides an important channel for research and career information exchange, which means a lot for early-stage researchers.

Sign up for the WiMIR mentoring programme because TRUST ME you will NOT regret it. It’s the best thing I have done for my future within my PhD.

Exposition second theme

Specific outcomes from mentoring sessions on career perspectives as reported by mentees

Quite a range of different projects have emerged from the mentoring sessions this year, from landing a job to programming skills, writing CVs, papers or research proposals, or getting an internship. Here are some examples.

I got a new job in the industry that relates to music! My mentor helped with all of the positive support and encouragement!

So, thanks to my mentor, I applied to the WiMIR Grant Application at the ISMIR Conference 2018.

I was able to land a job that combines music and computer science and I’m really excited to be making a difference in the music world from a variety of areas!

I received valuable feedback on my job materials such as my CV and a cover letter.

I learnt handy programming tricks.

I wrote and submitted a fellowship application (which got through to the final shortlist for the award).

I’ve learnt how to define and narrow a research problem and how to solve it step by step.

We could come up with a collaborative work on which we are currently working.

I presented a paper that I was working on for feedback to students/ faculty at University X.

I finally created a personal website.

Received help with a conference proposal and acceptance for conference.

Time saving! Great to have someone to help make yes/no decisions about whether opportunities are worth chasing or not.

Received help shaping my dissertation topic.

Started collaborating for a new paper.


Encouragement, encouragement, encouragement

An underlying topic that recurs over the editions of the mentoring program since 2016 is that of encouragement for mentees. Why is that so important? The psychiatrist Anna Fels has shown that ambition is built on two components: 1) mastering a skill, and 2) being recognized for it. Fels has demonstrated that being recognized by others for their skills happens to a much smaller extent for girls and women than for boys and men: “The personal and societal recognition they receive for their accomplishments is quantitatively poorer, qualitatively more ambivalent, and, perhaps most discouraging, less predictable.” Unfortunately, this starts already early for girls at schools: “Despite the fact that girls’ and women’s achievements, particularly in the academic sphere, frequently outstrip those of their male peers, they routinely underestimate their abilities. Boys and men, by contrast, have repeatedly been shown to have an inflated estimation of their capabilities. Paradoxically, these inaccurate self-ratings by both women and men seem to be accurate reflections of the praise and recognition they receive for their efforts. The impact of these findings on the selection and pursuit of an ambition is obvious: If you don’t think the chances are great that you will reach a career goal, you won’t attempt to reach it—even if the rewards are highly desirable.” (quotes from Anna Fels’ Harvard Business Review “Do Women Lack Ambition?”) More and more empirical studies reveal the different contexts in which women receive less recognition for the same skills as their male peers, such as the study by Moss-Racusin et al. (2012) which has shown that both male and female faculty rated male applicants as significantly more competent than women with identical application materials, and a study by Reuben et al. (2014) showing that both men and women were twice as likely to hire a man for a job that required math than a woman for that same job, even though the women performed equally well in an arithmetic test. Seeing, recognizing and rewarding the skills and talents of women seems to be an important ingredient to learn for all of us.

The WiMIR mentors pay an invaluable contribution toward encouraging mentees to follow their ambitions by doing exactly this: Seeing and recognizing their talents, showing possible career paths,  giving positive feedback on the mentees’ talents, and coming up with concrete steps such as those we have listed above in the exposition. At the same time, mentoring is a great way to discover female talent, and hence a big gain for the MIR community in getting to know these talented women and keep them hopefully involved in the field. Here are some mentees’ reflections on the encouragement this produces:

I have had one of the well-known, experienced MIR researchers all for myself – to talk about myself and help me set goals and develop a vision – what a luxury! I have emerged after my PhD without any understanding what I should do and where the field is going. I felt frustrated and disorientated and the positive, supportive attitude of my mentor was reassuring. Since then I have been on a journey of self-discovery and motivation and I am sure my mentor would be able to help me on several stages of this journey.

… helpful to talk to someone who has followed a career path that is similar to the one I plan to follow, and about which I had many doubts and fears.

I gained a mentor who has empowered me immensely.

I believe that the most important gain from the program was more confidence to work with MIR.

Now I could imagine myself researching interesting and relevant topics and going further in the academic carrier.

I became more optimistic as a Ph.D. student and have new insights to look at my research. The encouragements from my mentor mean a lot to me.

It encouraged me to try to stay in our field.

I felt empowered to ask questions openly and honestly, and felt like my mentor wanted to participate in our conversations just as much as I did. I felt valued and heard during our meetings.

It has opened so many doors for me, and built my confidence in networking in a competitive community.

The WiMIR mentoring has empowered myself.

Women in STEM are often unsure if it is okay to simultaneously feel assertive and vulnerable.  I was given the opportunity to ask questions and provide my own thoughts about STEM, MIR and other topics in a way that felt heard, respected and valued.  I got to practice asking questions in an open and trusting manner, which ultimately led me to understand that honesty, transparency and assertiveness (even in asserting that you are very confused and unsure about something) actually provide a platform for empowerment, respect and growth.

The programme shows you that you are not alone in MIR and STEM. Women are a minority, and this programme brings us together, it inspires and develops us as individuals and as a whole group. I feel that the programme brings confidence to new and aspiring researchers in the field, showing how we can get to the places we wish to reach.

Recapitulation first theme

Beyond the individual – effects of the program on the MIR community

One-on-one meetings in the mentoring program produce ripples beyond the individuals; they contribute to how the MIR community is perceived as a whole, as the following examples show:

I realized that the MIR community is wide, respectful and open to new members, even if they come from related but slightly different research domains.

If I had not applied for the WiMIR Mentoring Program, I probably wouldn’t know the amazing things that could be made from Music. This is the first place that I recommend to start learning and networking in the Music and Technology field.

MIR is a new field for me, but because WiMIR is here, I didn’t have to be scared to be a minority in a STEM field and MIR.

This is an important project to encourage new researchers to be in contact with important professionals and to develop new ideas. For women it is an opportunity to be visible and make more relevant works. I am very grateful for the excellent work of you organizers and I hope to meet you all at ISMIR 2018! =)

I’m really grateful to be attached to the community in this way even though I cannot yet make it to meetings in person. Thank you!

Because it was so easy to discuss things with my mentor, I found it easier to ask a question to other senior members of the MIR community.

Women have so many great ideas, and they bring different methods, perspectives and communication strategies to the table.  The more the women understand they are welcome and needed in MIR, the more they will stick around and be willing to dig deep.

If you don’t want to get lost in many keywords, this program will make you find your learning/career path.

Got to learn a lot from my mentor who is already established in this field. I also got referred to other people and got their feedback and guidance too.

Recapitulation second theme

The gain for mentors

The mentoring program is not only a gain for mentees; perhaps equally important are the gains for mentors. Here are some examples.

It’s really nice to interact with someone who is earlier in her career, and still has very many options to choose from and is also excited about them all. It’s easy to get lost in the day-to-day and forget why I’m doing what I do.

It made me be self-reflective in good ways.

Learning more about academic career paths in different cultures.

I wasn’t expecting to learn so much about recruiters, and the wide variety and competition of the job market.

The issues that women face are fundamentally different, even when they involve exactly the same scenario, just because of the way women are perceived in the workplace. I find that sometimes the approaches I might take as a man simply wouldn’t work for a woman, and it reveals that there is some underlying imbalance there.

It definitely makes me more aware of the gender imbalances and helps me refocus on efforts working with female students at my own institution.

The program helps me in reflecting my one role as an academic advisor.

I realised that all the prejudices that I need to deal with as a musicologist working with engineers are very similar to those an engineer had to face when working with musicologists.

Learned more on research cultures in other labs.

It was great to exchange ideas, links to reading material and perspectives. Hearing how people work in other companies and in academia was very interesting. Both my experience as a mentor and being involved in peer-mentoring were extremely eye opening.

… also learnt a lot about the challenges of raising a family and balancing that with work aspirations.

… learnt more about US universities, her industry experiences.

… a different perspective; insight into a different MIR subfield.

… a window into a different university system (in the USA).

I learned how to share industry experience with grads students.

… learning how to approach people who communicate differently.

I learned more about the obstacles of especially young females. We talked a lot about the many inappropriate statements by male colleagues and other people outside the work context.

It’s unfortunately common for women to encounter hostility and bias. Being a mentor can help balance the experience by demonstrating that not everyone has a negative attitude.


Future directions

Participants in the mentoring program came up with suggestions for further directions of the WiMIR initiative in their feedback forms, such as asking everyone to take the Harvard implicit associations test, asking industry sponsors to highlight their career paths for future female employees, having women-focused industry job fairs or network development, creating  videos about WiMIR, such as testimonial videos about the WiMIR Mentoring program and upload them on YouTube so many women can watch and learn about it and having more local meetups of mentees and mentors. We will discuss these ideas during the WiMIR session at ISMIR 2018 – and will need help realizing them!

Coda with closing fanfare

Fun for everybody involved in the program receiving praise in the feedback forms. Thanks everybody!

Running the mentoring program requires the dedication and time of the mentoring program committee, the mentors and the mentees. For most people, this is time spent on top of many other agenda points in a busy week. We hope the following quotes show to everybody how impactfully and meaningfully this time was spent, which brings us full circle to the opening fanfare of this report on the one hour-meeting that should have lasted longer.  

That was excellent. I will never forget this experience.

It was an excellent experience.

WiMIR Mentoring Program is So Awesome!

Awesome program!!

Just to say that I really enjoyed it, and I think it’s a fantastic initiative.

It was a good experience!

This is a great initiative, keep up the good work.

Love it. Thanks for making a cool program!

I would really like to thank WiMIR organizers for all the great work resulting in significant change in the field.

This mentorship program is one of the most effective ways to diversify the field of MIR, I hope this goes on for many years to come!

Thanks to the WiMIR team for the great concept and organizing this very impactful initiative.

Anja Volk (Utrecht University), holds master degrees in both mathematics and musicology, and a PhD in the field of computational musicology. The results of her research have substantially contributed to areas such as music information retrieval, computational musicology,  music cognition, and mathematical music theory.  In 2016 she launched together with Amélie Anglade, Emilia Gómez and Blair Kaneshiro the Women in MIR (WIMIR) Mentoring Program.  She co-organized the launch of the Transactions of the International Society for Music Information Retrieval, the open access journal of the ISMIR society, and is serving as Editor-in-Chief for the journal’s first term. Anja received the Westerdijk Award 2018 from Utrecht University in recognition of her efforts on increasing diversity.