Sebastian Böck

发表

Musical Onset Detection with Convolutional Neural Networks

Jan Schlüter, Sebastian Böck, 2013 .

MAXIMUM FILTER VIBRATO SUPPRESSION FOR ONSET DETECTION

G. Widmer, Sebastian Böck, 2013 .

On the Potential of Simple Framewise Approaches to Piano Transcription

Gerhard Widmer, Sebastian Böck, Andreas Arzt, 2016, ISMIR.

Multitask Learning for Polyphonic Piano Transcription, a Case Study

Gerhard Widmer, Sebastian Böck, Rainer Kelz, 2019, 2019 International Workshop on Multilayer Music Representation and Processing (MMRP).

Multi-Task Learning of Tempo and Beat: Learning One to Improve the Other

Matthew E. P. Davies, Peter Knees, Sebastian Böck, 2019, ISMIR.

Two Data Sets for Tempo Estimation and Key Detection in Electronic Dance Music Annotated from User Corrections

Peter Knees, Sebastian Böck, Perfecto Herrera, 2015, ISMIR.

Evaluating the Online Capabilities of Onset Detection Methods

Florian Krebs, Markus Schedl, Sebastian Böck, 2012, ISMIR.

madmom: A New Python Audio and Music Signal Processing Library

Florian Krebs, Gerhard Widmer, Sebastian Böck, 2016, ACM Multimedia.

Bridging the Audio-Symbolic Gap: The Discovery of Repeated Note Content Directly from Polyphonic Music Audio

Florian Krebs, Gerhard Widmer, Sebastian Böck, 2014, Semantic Audio.

Improved musical onset detection with Convolutional Neural Networks

Sebastian Böck, Jan Schlüter, Jan Schlüter, 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Joint Beat and Downbeat Tracking with Recurrent Neural Networks

Florian Krebs, Gerhard Widmer, Sebastian Böck, 2016, ISMIR.

Deconstruct, Analyse, Reconstruct: How to improve Tempo, Beat, and Downbeat Estimation

M. Davies, Sebastian Böck, 2020, ISMIR.

Rhythmic Pattern Modeling for Beat and Downbeat Tracking in Musical Audio

Florian Krebs, Gerhard Widmer, Sebastian Böck, 2013, ISMIR.

A low-latency, real-time-capable singing voice detection method with LSTM recurrent neural networks

Gerhard Widmer, Bernhard Lehner, Sebastian Böck, 2015, 2015 23rd European Signal Processing Conference (EUSIPCO).

Accurate Tempo Estimation Based on Recurrent Neural Networks and Resonating Comb Filters

Florian Krebs, Gerhard Widmer, Sebastian Böck, 2015, ISMIR.

Polyphonic piano note transcription with recurrent neural networks

Markus Schedl, Sebastian Böck, M. Schedl, 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

ENHANCED BEAT TRACKING WITH CONTEXT-AWARE NEURAL NETWORKS

Markus Schedl, Sebastian Böck, M. Schedl, 2011 .

Universal Onset Detection with Bidirectional Long Short-Term Memory Neural Networks

Björn W. Schuller, Alex Graves, Florian Eyben, 2010, ISMIR.

Deep Polyphonic ADSR Piano Note Transcription

Gerhard Widmer, Sebastian Böck, Rainer Kelz, 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Evaluating the Evaluation Measures for Beat Tracking

Matthew E. P. Davies, Sebastian Böck, M. Davies, 2014, ISMIR.

On the Importance of "Real" Audio Data for MIR Algorithm Evaluation at the Note-Level - A Comparative Study

Gerhard Widmer, Sebastian Böck, Bernhard Niedermayer, 2011, ISMIR.

Investigating the Similarity Space of Music Artists on the Micro-Blogosphere

Peter Knees, M. Schedl, Sebastian Böck, 2011, International Society for Music Information Retrieval Conference.

Fast Identification of Piece and Score Position via Symbolic Fingerprinting

Gerhard Widmer, Sebastian Böck, Andreas Arzt, 2012, ISMIR.

User-Driven Fine-Tuning for Beat Tracking

Sebastian Böck, Matthew Davies, António Pinto, 2021, Electronics.