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13th International Workshop on

Machine Learning and Music

September 18, 2020

Held in conjunction with ECML-PKDD 2020

MML 2020 Call for Papers



Machine learning and artificial intelligence have permeated nearly every area of music informatics, driven by a profusion of recordings available in digital audio formats, steady improvements to the accessibility and quality of symbolic corpora, availability of powerful algorithms in standard machine learning toolboxes, and theoretical advances in machine learning and data mining. As the complexity of the problems investigated by researchers on machine learning and music increases, there is a need to develop new algorithms and methods. As a consequence, research on machine learning and music is an active and growing field reflected in international meetings. MML 2020 is the 13th edition of the International Workshops on Machine Learning and Music (MML): 2008 (ICML, Helsinki, Finland), 2009 (ECML, Bled, Slovenia), 2010 (ACM-MM, Florence, Italy), 2011 (NIPS, Sierra Nevada, Spain), 2012 (ICML, Edinburgh, Scotland), 2013 (ECML/PKDD, Prague, Czech Republic), 2014 (Barcelona, Spain), 2015 (ISEA, Vancouver, Canada), 2016 (ECML/PKDD, Riva del Garda, Italy), 2017 (Barcelona, Spain), 2018 (ICML, Stockholm, Sweden), and 2019 (ECML, Würzburg, Germany).


The expected outcome of the workshop is to promote fruitful multidisciplinary collaboration among researchers who are using machine learning techniques in musical applications, by providing the opportunity to discuss ongoing work in the area. The expected attendees are active researchers in machine learning and music who have special interest in content-based music processing. 



Papers in all applications on music and machine learning are welcome, including but not limited to


  • Automatic classification of music (audio and MIDI) 

  • Style-based interpreter recognition

  • Music recommender systems, genre and tag prediction

  • Automatic score alignment

  • Polyphonic pitch detection

  • Chord extraction

  • Pattern discovery

  • Deep learning in music applications

  • Beat tracking

  • Expressive performance modeling

  • Statistical models for music prediction and generation *


* Extended versions of papers on this topic are welcomed for consideration for the Applied Sciences Special Issue on Statistical Models for Music Prediction and Generation.

Important Dates

Paper submission Deadline: July 30, 2020
Acceptance Notification: August 7, 2020
Workshop Date: September 18, 2020


MML 2020 will be held as a Virtual workshop.

Submissions of Papers

Papers of 4 printed pages in LNCS format are welcome. Papers will be evaluated according to their originality and relevance to the workshop, and should include author names, affiliations, contact information, and an abstract of 60-100 words.  Contributions should be in PDF format and submitted to

Rafael Ramirez, Universitat Pompeu Fabra, Spain
Darrell Conklin, University of the Basque Country, Spain
José Manuel Iñesta, University of Alicante, Spain


18 September 2020

9:00      Introduction
9:05      Mel-spectrogram Analysis to Identify Patterns in Musical Gestures: a Deep Learning Approach

             David Dalmazzo and Rafael Ramirez
9:15      Deep Learning vs. Traditional MIR: a Case Study on Musical Instrument Playing 

             Zehao Wang, Jingru Li, Xiaoou Chen, Zijin Li, Shicheng Zhang, Baoqiang Han, Deshun Yang
9:30      Adapted NMFD update procedure for removing double hits in drum mixture decompositions

             Len Vande Veire, Cedric De Boom, Tijl De Bie
9:45      Modeling Expressive Performance Deviations in Cello

             Tiange Zhu, Rafael Ramirez, and Sergio Giraldo
10:00    audioLIME: Listenable Explanations Using Source Separation

              Verena Haunschmid, Ethan Manilow, Gerhard Widmer
10:15    The Impact of Label Noise on a Music Tagger

              Katharina Prinz, Arthur Flexer, Gerhard Widmer
10:30    Geometric Deep Learning for Music Genre Classification

              Manoranjan Sathyamurthy, Xiaowen Dong, M. Pawan Kumar
10:45    Improving Audio Onset Detection for String Instruments by Incorporating Visual Modality

              Grigoris Bastas, Aggelos Gkiokas, Vassilis Katsouros, Petros Maragos
11:00    Audio textures in terms of generative models

              Lonce Wyse, Muhammad Huzaifah
11:15    Evaluation of different symbolic encodings for music generation with LSTM

              Manos Plitsis, Kosmas Kritsis, Maximos Kaliakatsos-Papakostas, Vassilis Katsouros
11:30    Medley2K: A Dataset of Medley Transitions

              Lukas Faber, Sandro Luck, Damian Pascual, Andreas Roth, Gino Brunner, Roger Wattenhofer

11:45    A Machine Learning Approach to Cross-cultural Children’s Songwriting Classification

              Kari Saarilahti, Rafael Ramirez
12:00    Two-step neural cross-domain experiments forfull-page recognition of Mensural documents

              Francisco J. Castellanos, Jorge Calvo-Zaragoza, and Jose M. Iñesta
12:15    Feature Engineering for Genre Characterization in Brazilian Music

              Bruna Wundervald
12:30    A dataset and classification model for Malay, Hindi, Tamil and Chinese music

              Fajilatun Nahar, Kat Agres, Balamurali BT, Dorien Herremans
12:45    Beat Tracking from Onset Streams Using LSTM Neural Networks

              Aggelos Gkiokas
13:00    Wrap up / End


You can find the MML2020 proceedings here.

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