Richard Vogl

Picture of Richard

DI Dr. techn. Richard Vogl
TU Wien
Faculty of Informatics
Institute of Information Systems Engineering
Information & Software Engineering Group
Favoritenstraße 9-11/194-1, 1040 Vienna, Austria

Office: Room HE 01 46
Hours: by appointment
Phone: +43 1 58801 188 309
e-mail: email adress

Short CV

Richard Vogl's research is situated at the intersection of music and artificial intelligence. He holds a PhD and masters degree in computer science with focus on machine learning, both from Johannes Kepler University (JKU), Linz. In the recent past he worked as a researcher at the Institute of Computational Perception at JKU Linz on the GiantSteps) project; as well as at the Faculty of Informatics at TU Wien on the SmarterJam project. His main interests are deep learning, signal processing, and music information research.

Publications

2019

Richard Vogl, Hamid Eghbal-zadeh, Peter Knees. An Automatic Drum Machine with Touch UI Based on a Generative Neural Network. In Demos and Posters Session of the 24th ACM Intelligent User Interfaces Conference (IUI), Los Angeles, CA, USA, 2019.

2018

Richard Vogl. Deep Learning Methods for Drum Transcription and Drum Pattern Generation. Doctoral Thesis.
Richard Vogl, Gerhard Widmer, and Peter Knees. Towards Multi-Instrument Drum Transcription. In Proceedings of the 21st International Conference on Digital Audio Effects (DAFx-18), Aveiro, Portugal, 2018.
Chih-Wei Wu, Christian Dittmar, Carl Southall, Richard Vogl, Gerhard Widmer, Jason Hockman, Meinard Müller, and Alexander Lerch. A Review of Automatic Drum Transcription. In: IEEE Transactions on Audio, Speech and Language Processing, vol. 26 nr. 9, pp. 1457—1483, (2018).
Richard Vogl*, Hamid Eghbal-Zadeh*, Gerhard Widmer, Peter Knees. GANs and Poses: An Interactive Generative Music Installation Controlled by Dance Moves. Interactive Machine-Learning for Music @Exhibition at ISMIR, Paris, France, 2018.
*Equal contribution.
Hamid Eghbal-Zadeh*, Richard Vogl*, Gerhard Widmer, Peter Knees. A GAN based Drum Pattern Generation UI Prototype. Late Breaking/Demos, 19th International Society for Music Information Retrieval Conference (ISMIR), Paris, France, 2018.
*Equal contribution.
Richard Vogl and Peter Knees. MIREX Submission For Drum Transcription 2018. MIREX submission abstracts, 2018.

2017

Richard Vogl, Matthias Dorfer, Gerhard Widmer, and Peter Knees. Drum transcription via joint beat and drum modeling using convolutional recurrent neural networks. In Proceedings of the 18th International Society for Music Information Retrieval Conference (ISMIR), Suzhou, China, 2017.
Richard Vogl, Matthias Dorfer, Gerhard Widmer, and Peter Knees. MIREX Submission for Drum Transcription 2017. MIREX submission abstracts, 2017.
Richard Vogl, Matthias Dorfer, and Peter Knees. Drum Transcription from Polyphonic Music with Recurrent Neural Networks. In Proceedings of the 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA, 2017.
Richard Vogl, and Peter Knees. An Intelligent Drum Machine for Electronic Dance Music Production and Performance. In Proceedings of The International Conference on New Interfaces for Musical Expression (NIME), Copenhagen, Denmark, 2017.

2016

Richard Vogl, Matthias Dorfer, and Peter Knees. Recurrent neural networks for drum transcription In Proceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR), New York, NY, USA, 2016.
Richard Vogl, Matthias Leimeister, Cárthach Ó Nuanáin, Sergi Jordà, Michael Hlatky, and Peter Knees. An Intelligent Interface for Drum Pattern Variation and Comparative Evaluation of Algorithms. In Journal of the Audio Engineering Society (JAES), Volume 64 Number 7/8, July/August,2016.
Richard Vogl, and Peter Knees. An Intelligent Musical Rhythm Variation Interface. In Demos and Posters Session of the 21st ACM Intelligent User Interfaces Conference (IUI), Sonoma, CA, USA, 2016.

2015

Peter Knees, Ángel Faraldo, Perfecto Herrera, Richard Vogl, Sebastian Böck, Florian Hörschläger, Mickael Le Goff. Two Data Sets For Tempo Estimation And Key Detection In Electronic Dance Music Annotated From User Corrections. In Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR), Málaga, Spain, 2015.
Florian Hörschläger, Richard Vogl, Sebastian Böck, and Peter Knees. Addressing Tempo Estimation Octave Errors in Electronic Music by Incorporating Style Information Extracted from Wikipedia. In Proceedings of the 12th Sound and Music Computing Conference (SMC). Maynooth, Ireland, 2015.

Talks

Richard Vogl. Drum transcription via joint beat and drum modeling using convolutional recurrent neural networks. At the 21st Vienna Deep Learning Meetup. Vienna, Austria, October 2018.
Richard Vogl. From Drum Transcription to Drum Pattern Generation. At the 1st Austrian Music Information Retrieval Workshop (AMIR). Vienna, Austria, June 2018.

Awards

2018

Best Documentation at the HAMR hackathon 2018 in Paris, for the software hack Neural Bubble Beat, by Florian Henkel, Filip Korzeniowski, Matthias Dorfer, and Richard Vogl.
Second best paper award at the 21st International Conference on Digital Audio Effects (DAFx-18), for the paper Towards Multi-Instrument Drum Transcription, by Richard Vogl, Gerhard Widmer, and Peter Knees.

2016

Best Gesture and Sound combination at the Waves Vienna Music Hackday 2016, for the hardware hack Metal Stance, by Richard Vogl and Daniel Hütter.

2015

Best Bitalino Hack and Best Juce Hack at the Music Hack Day at Sónar 2015 in Barcelona, for the hardware hack JucyPaintAlinoBrick, by Richard Vogl and Peter Knees.