A Re-ordering Strategy for Accelerating Index-based Audio Fingerprinting. - Hendrik Schreiber 001; Peter Grosche; Meinard Müller (ISMIR, 2011)

Exploiting global features for tempo octave correction. - Hendrik Schreiber 001; Meinard Müller (ICASSP, 2014)

Accelerating Index-Based Audio Identification. - Hendrik Schreiber 001; Meinard Müller (IEEE Trans. Multim., 2014)

Improving Genre Annotations for the Million Song Dataset. - Hendrik Schreiber 001 (ISMIR, 2015)

Genre Ontology Learning: Comparing Curated with Crowd-Sourced Ontologies. - Hendrik Schreiber 001 (ISMIR, 2016)

A Post-Processing Procedure for Improving Music Tempo Estimates Using Supervised Learning. - Hendrik Schreiber 001; Meinard Müller (ISMIR, 2017)

The MediaEval 2017 AcousticBrainz Genre Task: Content-based Music Genre Recognition from Multiple Sources. - Dmitry Bogdanov; Alastair Porter; Julián Urbano; Hendrik Schreiber 001 (MediaEval, 2017)

A Single-Step Approach to Musical Tempo Estimation Using a Convolutional Neural Network. - Hendrik Schreiber 001; Meinard Müller (ISMIR, 2018)

A Crowdsourced Experiment for Tempo Estimation of Electronic Dance Music. - Hendrik Schreiber 001; Meinard Müller (ISMIR, 2018)

The MediaEval 2018 AcousticBrainz Genre Task: Content-based Music Genre Recognition from Multiple Sources. - Dmitry Bogdanov; Alastair Porter; Julián Urbano; Hendrik Schreiber 001 (MediaEval, 2018)

Tempo-CNN Training Datasets (LMD Tempo, GiantSteps MTG Tempo, EBall). - Hendrik Schreiber 001; Meinard Müller (Zenodo, 2018)

New crowdsourced annotations for the GiantSteps Tempo dataset. - Hendrik Schreiber 001; Meinard Müller (Zenodo, 2018)

The AcousticBrainz Genre Dataset: Multi-Source, Multi-Level, Multi-Label, and Large-Scale. - Dmitry Bogdanov; Alastair Porter; Hendrik Schreiber 001; Julián Urbano; Sergio Oramas (ISMIR, 2019)

Towards Automatically Correcting Tapped Beat Annotations for Music Recordings. - Jonathan Driedger; Hendrik Schreiber 001; W. Bas de Haas; Meinard Müller (ISMIR, 2019)

Musical Tempo and Key Estimation using Convolutional Neural Networks with Directional Filters. - Hendrik Schreiber 001; Meinard Müller (CoRR, 2019)

Local Key Estimation In Classical Music Recordings: A Cross-Version Study on Schubert's Winterreise. - Hendrik Schreiber 001; Christof Weiß; Meinard Müller (ICASSP, 2020)

Modeling and Estimating Local Tempo: A Case Study on Chopin's Mazurkas. - Hendrik Schreiber 001; Frank Zalkow; Meinard Müller (ISMIR, 2020)

Local Key Estimation in Music Recordings: A Case Study Across Songs, Versions, and Annotators. - Christof Weiß; Hendrik Schreiber 001; Meinard Müller (IEEE ACM Trans. Audio Speech Lang. Process., 2020)

Music Tempo Estimation: Are We Done Yet? - Hendrik Schreiber 001; Julián Urbano; Meinard Müller (Trans. Int. Soc. Music. Inf. Retr., 2020)

Data-Driven Approaches for Tempo and Key Estimation of Music Recordings (Datengetriebene Verfahren für Tempo- und Tonart-Schätzung von Musikaufnahmen) - Hendrik Schreiber 001 (2020)