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ORCIDHendrik Schreiber 0001
Person information
- affiliation: Friedrich-Alexander-Universität Erlangen-Nürnberg, FAU, Germany
- affiliation (PhD 2020): University of Erlangen-Nuremberg, Semantic Audio Processing Group, Germany
- affiliation (2004): tagtraum industries inc., Raleigh, NC, USA
- affiliation (former): University of Dortmund, Germany
Other persons with the same name
- Hendrik Schreiber — disambiguation page
- Hendrik Schreiber 0002 — Paderborn University, Germany
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2020 – today
- 2020
[b1]Hendrik Schreiber:
Data-Driven Approaches for Tempo and Key Estimation of Music Recordings (Datengetriebene Verfahren für Tempo- und Tonart-Schätzung von Musikaufnahmen). University of Erlangen-Nuremberg, Germany, 2020- [j3]Christof Weiß
, Hendrik Schreiber, Meinard Müller:
Local Key Estimation in Music Recordings: A Case Study Across Songs, Versions, and Annotators. IEEE ACM Trans. Audio Speech Lang. Process. 28: 2919-2932 (2020) - [j2]Hendrik Schreiber, Julián Urbano, Meinard Müller:
Music Tempo Estimation: Are We Done Yet? Trans. Int. Soc. Music. Inf. Retr. 3(1): 111 (2020) - [c13]Hendrik Schreiber, Christof Weiß, Meinard Müller:
Local Key Estimation In Classical Music Recordings: A Cross-Version Study on Schubert's Winterreise. ICASSP 2020: 501-505 - [c12]Hendrik Schreiber, Frank Zalkow, Meinard Müller:
Modeling and Estimating Local Tempo: A Case Study on Chopin's Mazurkas. ISMIR 2020: 773-779
2010 – 2019
- 2019
- [c11]Jonathan Driedger, Hendrik Schreiber, W. Bas de Haas, Meinard Müller:
Towards Automatically Correcting Tapped Beat Annotations for Music Recordings. ISMIR 2019: 200-207 - [c10]Dmitry Bogdanov, Alastair Porter, Hendrik Schreiber, Julián Urbano, Sergio Oramas:
The AcousticBrainz Genre Dataset: Multi-Source, Multi-Level, Multi-Label, and Large-Scale. ISMIR 2019: 360-367 - [i1]Hendrik Schreiber, Meinard Müller:
Musical Tempo and Key Estimation using Convolutional Neural Networks with Directional Filters. CoRR abs/1903.10839 (2019) - 2018
- [c9]Hendrik Schreiber, Meinard Müller:
A Single-Step Approach to Musical Tempo Estimation Using a Convolutional Neural Network. ISMIR 2018: 98-105 - [c8]Hendrik Schreiber, Meinard Müller:
A Crowdsourced Experiment for Tempo Estimation of Electronic Dance Music. ISMIR 2018: 409-415 - [c7]Dmitry Bogdanov, Alastair Porter, Julián Urbano, Hendrik Schreiber:
The MediaEval 2018 AcousticBrainz Genre Task: Content-based Music Genre Recognition from Multiple Sources. MediaEval 2018 - [d2]Hendrik Schreiber, Meinard Müller:
Tempo-CNN Training Datasets (LMD Tempo, GiantSteps MTG Tempo, EBall). Zenodo, 2018
- [d1]Hendrik Schreiber, Meinard Müller:
New crowdsourced annotations for the GiantSteps Tempo dataset. Zenodo, 2018 - 2017
- [c6]Hendrik Schreiber, Meinard Müller:
A Post-Processing Procedure for Improving Music Tempo Estimates Using Supervised Learning. ISMIR 2017: 235-242 - [c5]Dmitry Bogdanov, Alastair Porter, Julián Urbano, Hendrik Schreiber:
The MediaEval 2017 AcousticBrainz Genre Task: Content-based Music Genre Recognition from Multiple Sources. MediaEval 2017 - 2016
- [c4]Hendrik Schreiber:
Genre Ontology Learning: Comparing Curated with Crowd-Sourced Ontologies. ISMIR 2016: 400-406 - 2015
- [c3]Hendrik Schreiber:
Improving Genre Annotations for the Million Song Dataset. ISMIR 2015: 241-247 - 2014
- [j1]Hendrik Schreiber, Meinard Müller:
Accelerating Index-Based Audio Identification. IEEE Trans. Multim. 16(6): 1654-1664 (2014) - [c2]Hendrik Schreiber, Meinard Müller:
Exploiting global features for tempo octave correction. ICASSP 2014: 639-643 - 2011
- [c1]Hendrik Schreiber, Peter Grosche, Meinard Müller:
A Re-ordering Strategy for Accelerating Index-based Audio Fingerprinting. ISMIR 2011: 127-132
Coauthor Index
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