Skip to main content
School of Electronic Engineering and Computer Science
  1. School of Electronic Engineering and Computer Science
  2. People
  3. Profiles

Dr Johan Pauwels

Johan

Lecturer in Audio Signal Processing

Email: j.pauwels@qmul.ac.uk
Room Number: Engineering, Eng E108
Office Hours: by appointment

Profile

Johan Pauwels is a Lecturer with the Centre for Digital Music at Queen Mary University of London. He received Master of Science degrees in Electrical/Electronics Engineering (KU Leuven ’06) and Artificial Intelligence (KU Leuven ’07). In 2016, he obtained a PhD from Ghent University on the topic of automatic harmony recognition from audio. He has further held research positions at IRCAM and Imperial College of London and has also taught at City, University of London and the University of West London.

Teaching

In 2023-2024, I am teaching

  • ECS7013P Deep Learning for Audio and Music (MSc/PhD)
  • ECS411U Signals and Information (1st year UG)

Research

Research Interests:

Johan’s aim is to make machines understand music to the level of a trained professional, such that new tools can be developed to assist musicians performing live or in the studio, listeners navigating large music collections and learners studying music. To that end, he uses a combination of machine learning, signal processing, data science and music theory. In recent years, he has been working on narrowing the gap between academic research and user-centric applications, web-based music services and the personalisation of spatial and immersive audio.

Publications

  • Engel I, Daugintis R, Vicente T et al. (publicationYear). The SONICOM HRTF Dataset. nameOfConference


  • Pauwels J, Picinali L (2023). On the relevance of the differences between HRTF measurement setups for machine learning. 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023)


  • Turchet L, Zanotto C, Pauwels J (2023). “Give me happy pop songs in C major and with a fast tempo”: A vocal assistant for content-based queries to online music repositories. nameOfConference


  • Turchet L, Pauwels J (2022). Music Emotion Recognition: Intention of Composers-Performers Versus Perception of Musicians, Non-Musicians, and Listening Machines. nameOfConference


  • Buffa M, Cabrio E, Fell M et al. (2021). The WASABI Dataset: Cultural, Lyrics and Audio Analysis Metadata About 2 Million Popular Commercially Released Songs. nameOfConference


    QMRO: qmroHref
  • Turchet L, Pauwels J, Fischione C et al. (2020). Cloud-smart Musical Instrument Interactions. nameOfConference


  • Pauwels J, O'Hanlon K, Gómez E et al. (2019). 20 Years of Automatic Chord Recognition from Audio. Proceedings of the 20th Conference of the International Society for Music Information Retrieval (ISMIR)

    DOI: doi

  • Senvaityte D, Pauwels J, Sandler M (2019). Guitar String Separation Using Non-Negative Matrix Factorization and Factor Deconvolution. Audio Mostly 2019


  • Pauwels J, Sandler M (2019). Finding new practice material through chord-based exploration of a large music catalogue. Proceedings of the 16th Sound and Music Conference

    DOI: doi

    QMRO: qmroHref
  • Pauwels J, Sandler MB (2019). A web-based system for suggesting new practice material to music learners based on chord content. 2nd Workshop on Intelligent Music Interfaces for Listening and Creation

    DOI: doi

  • Pauwels J, Xambó A, Roma G et al. (2018). Exploring Real-time Visualisations to Support Chord Learning with a Large Music Collection. 4th Web Audio Conference (WAC)

    DOI: doi

  • Pauwels J, Sandler M (2018). pywebaudioplayer: Bridging the gap between audio processing code and attractive visualisations based on web technology. 4th Web Audio Conference (WAC)

    DOI: doi

  • Xambó A, Pauwels J, Roma G et al. (2018). Jam with Jamendo. Proceedings of the Audio Mostly 2018 on Sound in Immersion and Emotion


    QMRO: qmroHref
  • Pauwels J, Fazekas G, Sandler M (2018). Recommending songs to music learners based on chord content. 2018 Joint Workshop on Machine Learning for Music

    DOI: doi

  • Xi Q, Bittner RM, Pauwels J et al. (2018). Guitarset: A dataset for guitar transcription. nameOfConference

    DOI: doi

    QMRO: qmroHref
  • Xi Q, Bittner RM, PAUWELS J et al. (2017). Guitar-set preview: a dataset for guitar transcription and more. 18th Conference of the International Society for Music Information Retrieval (ISMIR): Late Breaking/Demo Session

    DOI: doi

  • O'Hanlon KO, Ewert S, Pauwels J et al. (2017). Improved template-based chord recognition using the CRP feature. 2017 IEEE Conference on Acoustics, Speech and Signal Processing


  • Pauwels J, O hanlon K, Fazekas G et al. (2017). Confidence Measures and Their Applications in Music Labelling Systems Based on Hidden Markov Models. nameOfConference

    DOI: doi

  • Peeters G, Pauwels J (2016). Chord Recognition. nameOfConference


    QMRO: qmroHref
  • PAUWELS J (2016). Exploiting prior knowledge during automatic key and chord estimation from musical audio. Ghent University

    DOI: doi

    QMRO: qmroHref
  • Pauwels J, Martens J-P (2014). Combining Musicological Knowledge About Chords and Keys in a Simultaneous Chord and Local Key Estimation System. nameOfConference


    QMRO: qmroHref
  • Burgoyne JA, de Haas WB, Pauwels J (2014). On comparative statistics for labelling tasks: what can we learn from MIREX ACE 2013?. 15th Conference of the International Society for Music Information Retrieval (ISMIR)

    DOI: doi

    QMRO: qmroHref
  • Pauwels J, Peeters G (2013). Segmenting music through the joint estimation of keys, chords and structural boundaries. Proceedings of the 21st ACM international conference on Multimedia


    QMRO: qmroHref
  • Pauwels J, Peeters G (2013). EVALUATING AUTOMATICALLY ESTIMATED CHORD SEQUENCES. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing


    QMRO: qmroHref
  • Pauwels J, Kaiser F, Peeters G (2013). Combining harmony-based and novelty-based approaches for structural segmentation. 14th Conference of the International Society for Music Information Retrieval (ISMIR)

    DOI: doi

    QMRO: qmroHref
  • Pauwels J, Martens J-P, Leman M (2011). The influence of chord duration modeling on chord and local key extraction. 2011 10th International Conference on Machine Learning and Applications and Workshops


    QMRO: qmroHref
  • Pauwels J, Martens J-P, Leman M (2011). MODELING MUSICOLOGICAL INFORMATION AS TRIGRAMS IN A SYSTEM FOR SIMULTANEOUS CHORD AND LOCAL KEY EXTRACTION. 2011 IEEE International Workshop on Machine Learning for Signal Processing


    QMRO: qmroHref
  • Pauwels J, Martens J-P, Leman M (2011). Improving the key extraction performance of a simultaneous local key and chord estimation system. IEEE International Conference on Multimedia and Expo (ICME)


    QMRO: qmroHref
  • Pauwels J, Martens JP (2010). Integrating musicological knowledge into a probabilistic framework for chord and key extraction. nameOfConference

    DOI: doi

    QMRO: qmroHref
  • Varewyck M, Pauwels J, Martens J-P (2008). A novel chroma representation of polyphonic music based on multiple pitch tracking techniques. Proceedings of the 16th ACM international conference on Multimedia


    QMRO: qmroHref

Supervision

Currently, I'm part of the supervisory team of the following PhD students:

  • Yannis Vasilakis (primary supervisor)
  • James Bolt (secondary supervisor)
  • Carlos De La Vega Martin (secondary supervisor)
  • Jeff Miller (secondary supervisor)
  • Kasia Adamska (secondary supervisor)
  • Harnick Khera (secondary supervisor)

Most of these are funded through the UKRI Doctoral School in AI and Music.

Moreover, in a typical year I am supervising 8-10 BSc/BEng and 8-10 MSc students for their final year projects.

Back to top