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.

Anja Volk awarded this year’s Westerdijk Award

Blog post written by Vincent Koops. 
Dr. Anja Volk, one of WiMIR leading scholars, is awarded this year’s Westerdijk Award at the Utrecht University in recognition of her efforts to create a more diverse organization. Besides her efforts in building the WiMIR network, she established the Women in Information and Computing Science network. This network is very active in organizing events that stimulate more diversity and inclusion within the department and in applying for funding to finance these events. Anja is furthermore a major contributor to develop concrete recommendations for more diverse leadership within her department. Anja is deeply appreciated because of her passionate efforts to improve diversity and inclusion within and beyond Utrecht University!
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You can read the complete news here: https://www.uu.nl/en/news/anja-volk-wins-westerdijk-award

WiMIR Workshop 2018 Project Guides

This is a list of project guides and their areas of interest for the 2018 WiMIR workshop.  These folks will be leading the prototyping and early research investigations at the workshop.  You can read about them and their work in detail below, and sign up to attend the WiMIR workshop here.



Rachel Bittner:  MIR with Stems

The majority of digital audio exists as mono or stereo mixtures, and because of this MIR research has largely focused on estimating musical information (beats, chords, melody, etc.) from these polyphonic mixtures. However, stems (the individual components of a mixture) are becoming an increasingly common audio format. This project focuses on how MIR techniques could be adapted if stems were available for all music. Which MIR problems suddenly become more important? What information – that was previously difficult to estimate from mixtures – is now simple to estimate? What new questions can we ask about music that we couldn’t before? As part the project, we will try to answer some of these questions and create demos that demonstrate our hypotheses.

Rachel is a Research Scientist at Spotify in New York City, and recently completed her Ph.D. at the Music and Audio Research Lab at New York University under Dr. Juan P. Bello. Previously, she was a research assistant at NASA Ames Research Center working with Durand Begault in the Advanced Controls and Displays Laboratory. She did her master’s degree in math at NYU’s Courant Institute, and her bachelor’s degree in music performance and math at UC 2 Irvine. Her research interests are at the intersection of audio signal processing and machine learning, applied to musical audio. Her dissertation work applied machine learning to various types of fundamental frequency estimation.

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Tom Butcher: Expanding the Human Impact of MIR with Mixed Reality

Mixed reality has the potential to transform our relationship with music. In this workshop, we will survey the new capabilities mixed reality affords as a new computing paradigm and explore how these new affordances can open the world of musical creation, curation, and enjoyment to new vistas. We will begin by discussing what mixed reality means, from sensors and hardware to engines and platforms for mixed reality experiences. From there, we will discuss how mixed reality can be applied to MIR- related fields of study and applications, considering some of the unique challenges and new research questions posed by the technology. Finally, we will discuss human factors and how mixed reality coupled with MIR can lead to greater understanding, empathy, expression, enjoyment, and fulfillment.

Tom Butcher leads a team of engineers applied scientists in Microsoft’s Cloud & AI division focusing on audio sensing, machine listening, avatars, and applications of AI. In the technology realm, Tom is an award-winning creator of audio and music services, which include recommendation engines, continuous playlist systems, assisted composition agents, and other tools for creativity and productivity. Motivated by a deep enthusiasm for synthesizers and electronic sounds from an early age, Tom has released many pieces of original music as Orqid and Codebase and continues to record and perform. In 2015, Tom co- founded a Seattle-based business focusing on community, education, and retail for synthesizers and electronic music instruments called Patchwerks.


Elaine Chew: MIR Rhythm Analysis Techniques for Arrhythmia ECG Sequences

Cardiac arrhythmia has been credited as the source of the dotted rhythm at the beginning of Beethoven’s “Adieux” Sonata (Op.81a) (Goldberger, Whiting, Howell 2014); the authors have also ascribed Beethoven’s “Cavatina” (Op.130) and another piano sonata (Op.110) to his possible arrhythmia. It is arguably problematic and controversial to diagnose arrhythmia in a long-dead composer through his music. Without making any hypothesis on composers’ cardiac conditions, Chew (2018) linked the rhythms of trigeminy (a ventricular arrhythmia) to the Viennese Waltz and scored atrial fibrillation rhythms to mixed meters, Bach’s Siciliano, and the tango; she also made collaborative compositions (Chew et al. 2017-8) from longer ventricular tachycardia sequences. Given the established links between heart and musical rhythms, in this workshop, we shall take the pragmatic and prosaic approach of applying a wide variety of MIR rhythm analysis techniques to ECG recordings of cardiac arrhythmias, exploring the limits of what is currently possible.

Chew, E. (2018). Notating Disfluencies and Temporal Deviations in Music and Arrhythmia. Music and Science. [ html | pdf ]
Chew, E., A. Krishna, D. Soberanes, M. Ybarra, M. Orini, P. Lambiase (2017-8). Arrhythmia Suitebit.ly/heart-music-recordings
Goldberger, Z. D., S. M. Whiting, J. D. Howell (2014). The Heartfelt Music of Ludwig van Beethoven. Perspectives in Biology and Medicine, 57(2): 285-294. [synopsis]

Elaine Chew is Professor of Digital Media at Queen Mary University of London, where she is affiliated with the Centre for Digital Music in the School of Electronic Engineering and Computer Science. She was awarded a 2018 ERC ADG for the project COSMOS: Computational Shaping and Modeling of Musical Structures, and is recipient of a 2005 Presidential Early Career Award in Science and Engineering / NSF CAREER Award, and 2007/2017 Fellowships at Harvard’s Radcliffe Institute for Advanced Studies. Her research, which centers on computational analysis of music structures in performed music, performed speech, and cardiac arrhythmias, has been supported by the ERC, EPSRC, AHRC, and NSF, and featured on BBC World Service/Radio 3, Smithsonian Magazine, Philadelphia Inquirer, Wired Blog, MIT Technology Review, etc. She has authored numerous articles and a Springer monograph (Mathematical and Computational Modeling of Tonality: Theory and Applications), and served on the ISMIR steering committee.



Johanna Devaney:  Cover Songs for Musical Performance Comparison and Musical Style Transfer

Cover versions of a song typically retain basic musical the material of the song being covered but may vary a great deal in their fidelity to other aspects of the original recording. While some covers only differ in minor ways, such as timing and dynamics, while others may use completely different instrumentation, performance techniques, or genre. This workshop will explore the potential of cover songs for studying musical performance and for performing musical style transfer. In contrast to making comparisons between different performances of different songs, cover songs provide a unique opportunity to evaluate differences in musical performance, both within and across genres. For musical style transfer, the stability of the musical material serves as an invariant representation, which allows for paired examples for training machine learning algorithms. The workshop will consider issues in dataset creation as well as metrics for evaluating performance similarity and style transfer.

Johanna is an Assistant Professor of Music Technology at Brooklyn College, City University of New York and the speciality chief editor for the Digital Musicology section of Frontiers in Digital Humanities. Previously she taught in the Music Technology program at NYU Steinhardt and the Music Theory and Cognition program at Ohio State University. Johanna completed her post-doc at the Center for New Music and Audio Technologies (CNMAT) at the University of California at Berkeley and her PhD in music technology at the Schulich School of Music of McGill University. She also holds an MPhil degree in music theory from Columbia University, as well as an MA in composition from York University in Toronto. Johanna’s research seeks to understand how humans engage with music, primarily through performance, with a particular focus on intonation in the singing voice, and how computers can be used to model and augment our understanding of this engagement.


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Doug Eck: Building Collaborations Among Artists, Coders and Machine Learning

We propose to talk about challenges and future directions for building collaborations among artists, coders and machine learning researchers. The starting point is g.co/magenta. We’ve learned a lot about what works and (more importantly) what doesn’t work in building bridges across these areas. We’ll explore community building, UX/HCI issues, research directions, open source advocacy and the more general question of deciding what to focus on in such an open-ended, ill-defined domain. We hope that the session is useful even for people who don’t know of or don’t care about Magenta. In other words, we’ll use Magenta as a starting point for exploring these issues, but we don’t need to focus solely on that project.

Douglas Eck is a Principal Research Scientist at Google working in the areas of music, art and machine learning. Currently he is leading the Magenta Project, a Google Brain effort to generate music, video, images and text using deep learning and reinforcement learning. One of the primary goals of Magenta is to better understand how machine learning algorithms can learn to produce more compelling media based on feedback from artists, musicians and consumers. Before focusing on generative models for media, Doug worked in areas such as rhythm and meter perception, aspects of music performance, machine learning for large audio datasets and music recommendation for Google Play Music. He completed his PhD in Computer Science and Cognitive Science at Indiana University in 2000 and went on to a postdoctoral fellowship with Juergen Schmidhuber at IDSIA in Lugano Switzerland. Before joining Google in 2010, Doug worked in Computer Science at the University of Montreal (MILA machine learning lab) where he became Associate Professor.



Ryan Groves:  Discovering Emotion from Musical Segments

In this project, we’ll first survey the existing literature for research on detecting emotions from musical audio, and find relevant software tools and datasets to assist in the process. Then, we’ll try to formalize our own expertise in how musical emotion might be perceived, elicited and automatically evaluated from musical audio. The goal of the project will be to create a software service or tool that can take a musical audio segment that is shorter than a whole song, and detect the emotion from it.

Ryan Groves is an award-winning music researcher and veteran developer of intelligent music systems. He did a Masters’ in Music Technology at McGill University under Ichiro Fujinaga, has published in conference proceedings including Mathematics and Computation in Music, Musical Metacreation (ICCC & AIIDE), and ISMIR. In 2016, he won the Best Paper award at ISMIR for his paper on “Automatic melodic reduction using a supervised probabilistic context-free grammar”.  He is currently the President and Chief Product Officer at Melodrive – an adaptive music generation system. Using cutting-edge artificial intelligence techniques, Melodrive allows any developer to automatically create and integrate a musical soundtrack into their game, virtual world or augmented reality system.  With a strong technical background, extensive industry experience in R&D, and solid research footing in academia, Ryan is focused on delivering innovative and robust musical products.



Christine Ho, Oriol Nieto, & Kristi Schneck:  Large-scale Karaoke Song Detection

We propose to investigate the problem of automatically identifying Karaoke tracks in a large music catalog. Karaoke songs are typically instrumental renditions of popular tracks, often including backing vocals in the mix, such that a live performer can sing on top of them. The automatic identification of such tracks would not only benefit the curation of large collections, but also its navigation and exploration. We challenge the participants to think about the type of classifiers we could use in this problem, what features would be ideal, and what dataset would be beneficial to the community to potentially propose this as a novel MIREX (MIR Evaluation eXchange) task in the near future.

Oriol Nieto is a Senior Scientist at Pandora. Prior to that, he defended his Ph.D Dissertation in the Music and Audio Research Lab at NYU focusing on the automatic analysis of structure in music. He holds an M.A. in Music, Science and Technology from the Center for Computer Research in Music and Acoustics at Stanford University, an M.S. in Information Theories from the Music Technology Group at Pompeu Fabra University, and a Bachelor’s degree in Computer Science from the Polytechnic University of Catalonia. His research focuses on music information retrieval, large scale recommendation systems, and machine learning with especial emphasis on deep architectures. Oriol plays guitar, violin, and sings (and screams) in his spare time.

Kristi Schneck is a Senior Scientist at Pandora, where she is leading several science initiatives on Pandora’s next-generation podcast recommendation system. She has driven the science work for a variety of applications, including concert recommendations and content management systems. Kristi holds a PhD in physics from Stanford University and dual bachelors degrees in physics and music from MIT.

Christine Ho is a scientist on Pandora’s content science team, where she works on detecting music spam and helps teams with designing their AB experiments. Before joining Pandora, she completed her PhD in Statistics at University of California, Berkeley and interned at Veracyte, a company focused on applying machine learning to genomic data to improve outcomes for patients with hard-to-diagnose diseases.


Xiao Hu: MIR for Mood Modulation: A Multidisciplinary Research Agenda

Mood modulation is a main reason behind people’s engagement with music, whereas how people use music to modulate mood and how MIR techniques and systems can facilitate this process continue fascinating researchers in various related fields. In this workshop group, we will discuss how MIR researchers with diverse backgrounds and interests can participate in this broad direction of research. Engaging activities are designed to enable hands-on practice on multiple research methods and study design (both qualitative and quantitative/computational). Through feedback from peers and the project guide, participants are expected to start developing a focused research agenda with theoretical, methodological and practical significance, based on their own strengths and interests. Participants from different disciplines and levels are all welcomed. Depending on the background and interests of the participants, a small new dataset is prepared for fast prototyping on how MIR techniques and tools can help enhancing this multidisciplinary research agenda.

Dr. Xiao Hu has been studying music mood recognition and MIR evaluation since 2006. Her research on affective interactions between music and users has been funded by the National Science Foundation of China and Research Grant Council (RGC) of the Hong Kong S. A. R. Dr. Hu was a tutorial speaker in ISMIR conferences in 2012 and 2016. Her papers have won several awards in international conferences and have been cited extensively. She has served as a conference co-chair (2014), a program co-chair (2017 and 2018) for ISMIR, and an editorial board member of TISMIR. She was in the Board of Directors of ISMIR from 2012 to 2017. Dr. Hu has a multidisciplinary background, holding a PhD degree in Library and Information Science, Multi-disciplinary Certificate in Language and Speech Processing, and a Master’s degree in Computer Science, a Master’s degree in Electrical Engineering and a Bachelor’s degree in Electronics and Information Systems.

Anja Volk, Iris Yuping Ren, & Hendrik Vincent Koops:  Modeling Repetition and Variation for MIR

Repetition and variation are fundamental principles in music. Accordingly, many MIR tasks are based on automatically detecting repeating units in music, such as repeating time intervals that establish the beat, repeating segments in pop songs that establish the chorus, or repeating patterns that constitute the most characteristic part of a composition. In many cases, repetitions are not literal, but subject to slight variations, which introduces the challenge as to what types of variation of a musical unit can be reasonably considered as a re-occurrence of this unit. In this project we look into the computational modelling of rhythmic, melodic, and harmonic units, and the challenge of evaluating state-of-the-art computational models by comparing the output to human annotations. Specifically, we investigate for the MIR tasks of 1) automatic chord extraction from audio, and 2) repeated pattern discovery from symbolic data, how to gain high-quality human annotations which account for different plausible interpretations of complex musical units. In this workshop we discuss different strategies of instructing annotators and undertake case studies on annotating patterns and chords on small data sets. We compare different annotations, jointly reflect on the rationales regarding these annotations, develop novel ideas on how to setup annotation tasks and discuss the implications for the computational modelling of these musical units for MIR.

Anja Volk holds masters degrees in both Mathematics and Musicology, and a PhD from Humboldt University Berlin, Germany. Her area of specialization is the development and application of computational and mathematical models for music research. The results of her research have substantially contributed to areas such as music information retrieval, computational musicology, digital cultural heritage, music cognition, and mathematical music theory. In 2003 she has been awarded a Postdoctoral Fellowship Award at the University of Southern California, in 2006 she joined Utrecht University as a Postdoc in the area of Music Information Retrieval. In 2010 she has been awarded a highly prestigious NWO-VIDI grant from the Netherlands Organisation for Scientific Research, which allowed her to start her own research group. In 2016 she co-launched the international Women in MIR mentoring program, in 2017 she co-organized the launch of the Transactions of the International Society for Music Information Retrieval, and is serving as Editor-in-Chief for the journal’s first term.

Cynthia C. S. Liem & Andrew Demetriou:  Beyond the Fun: Can Music We Do Not Actively Like Still Have Personal Significance?

In today’s digital information society,music is typically perceived and framed as ‘mere entertainment’. However, historically, the significance of music to human practitioners and listeners has been much broader and more profound. Music has been used to emphasize social status, to express praise or protest, to accompany shared social experiences and activities, and to moderate activity, mood and self-established identity as a ‘technology of the self’. Yet today, our present-day music services (and their underlying Music Information Retrieval (MIR) technology) do not focus explicitly on fostering these broader effects: they may be hidden in existing user interaction data, but this data usually lacks sufficient context to tell for sure.  As a controversial thought, music that is appropriate for the scenarios above may not necessarily need to be our favorite music, yet still be of considerable personal value and significance to us. How can and should we deal with this in the context of MIR and recommendation? May MIR systems then become the tools that can surface such items, and thus create better user experiences that users could not have imagined themselves? What ethical and methodological considerations should we take into account when pursuing this? And, for technologists in need of quantifiable and measurable criteria of success, how should the impact of suggested items on users be measured in these types of scenarios?   In this workshop, we will focus on discussing these questions from an interdisciplinary perspective, and jointly designing corresponding initial MIR experimental setups.

Cynthia Liem graduated in Computer Science at Delft University of Technology, and in Classical Piano Performance at the Royal Conservatoire in The Hague. Now an Assistant Professor at the Multimedia Computing Group of Delft University of Technology, her research focuses on music and multimedia search and recommendation, with special interest in fostering the discovery of content which is not trivially on users’ radars. She gained industrial experience at Bell Labs Netherlands, Philips Research and Google, was a recipient of multiple scholarships and awards (e.g. Lucent Global Science & Google Anita Borg Europe Memorial scholarships, Google European Doctoral Fellowship, NWO Veni) and is a 2018 Researcher-in-Residence at the National Library of The Netherlands. Always interested in discussion across disciplines, she also is co-editor of the Multidisciplinary Column of the ACM SIGMM Records. As a musician, she still has an active performing career, particularly with the (inter)nationally award-winning Magma Duo.

Andrew Demetriou is currently a PhD candidate in the Multimedia Computing Group at the Technical University at Delft. His academic interests lie in the intersection of the psychological and biological sciences, and the relevant data sciences, and furthering our understanding of 1) love, relationships, and social bonding, and 2) optimal, ego-dissolutive, and meditative mental states, 3) by studying people performing, rehearsing, and listening to music. His prior experience includes: assessing the relationship between initial romantic attraction and hormonal assays (saliva and hair) during speed-dating events, validating new classes of experimental criminology VR paradigms using electrocardiography data collected both in a lab and in a wild setting (Lowlands music festival), and syntheses of musical psychology literature which were presented at ISMIR 2016 and 2017.



Matt McVicar: Creative applications of MIR Data

In this workshop, you’ll explore the possibility of building creative tools using MIR data. You’ll discuss the abundance of prevailing data for creative applications, which in the context of this workshop simply means “a human making something musical”. You, as a team, may come up with new product or research ideas based on your own backgrounds, or you may develop an existing idea from existing products or research papers. You may find that the data for your application exists already, so that you can spend the time in the workshop fleshing out the details of how your application will work. Else, you may discover that the data for your task does not exist, in which case you, as a team, could start gathering or planning the gathering of these data.

Matt is Head of Research at Jukedeck. He began his PhD at the University of Bristol under the supervision of Tijl De Bie and finished it whilst on a Fulbright Scholarship at Columbia University in the city of New York with Dan Ellis. He then went on to work under Masataka Goto at the National Institute for Advanced Industrial Science and Technology in Tsukuba, Japan. Subsequently, he returned to Bristol to undertake a 2 year grant in Bristol. He joined Jukedeck in April 2016, and his main interests are the creative applications of MIR to domains such as algorithmic composition.