| A Single-Step Approach to Musical Tempo Estimation Using a Convolutional Neural Network H Schreiber, M Müller Proceedings of the International Society for Music Information Retrieval …, 2018 | 73 | 2018 |
| Improving Genre Annotations for the Million Song Dataset H Schreiber Proceedings of the International Society for Music Information Retrieval …, 2015 | 61 | 2015 |
| Musical Tempo and Key Estimation using Convolutional Neural Networks with Directional Filters H Schreiber, M Müller Sound and Music Computing Conference (SMC), Málaga, Spain, 2019 | 38 | 2019 |
| Java Server und Servlets: portierbare Web-Applikationen effizient entwickeln;[inklusive Framework für den Bau eines webbasierten Java-Applikationsservers] P Roßbach, H Schreiber Addison-Wesley, 1999 | 35* | 1999 |
| Local Key Estimation in Music Recordings: A Case Study Across Songs, Versions, and Annotators C Weiß, H Schreiber, M Müller IEEE/ACM Transactions on Audio, Speech and Language Processing 28, 2919-2932, 2020 | 32 | 2020 |
| The AcousticBrainz genre dataset: Multi-source, multi-level, multi-label, and large-scale D Bogdanov, A Porter, H Schreiber, J Urbano, S Oramas Proceedings of the International Society for Music Information Retrieval …, 2019 | 29 | 2019 |
| Music Tempo Estimation: Are We Done Yet? H Schreiber, J Urbano, M Müller Transactions of the International Society for Music Information Retrieval …, 2020 | 23 | 2020 |
| A Crowdsourced Experiment for Tempo Estimation of Electronic Dance Music H Schreiber, M Müller Proceedings of the International Society for Music Information Retrieval …, 2018 | 20 | 2018 |
| Modeling and Estimating Local Tempo: A Case Study on Chopin’s Mazurkas H Schreiber, F Zalkow, M Müller Proceedings of the International Society for Music Information Retrieval …, 2020 | 19 | 2020 |
| The MediaEval 2018 AcousticBrainz genre task: Content-based music genre recognition from multiple sources D Bogdanov, A Porter, J Urbano, H Schreiber | 19 | 2018 |
| A Post-Processing Procedure for Improving Music Tempo Estimates Using Supervised Learning H Schreiber, M Müller Proceedings of the International Society for Music Information Retrieval …, 2017 | 19 | 2017 |
| The MediaEval 2017 AcousticBrainz Genre Task: Content-based Music Genre Recognition from Multiple Sources D Bogdanov, A Porter, J Urbano, H Schreiber MediaEval 2017 Workshop, Dublin, Ireland, 2017 | 19 | 2017 |
| Local Key Estimation In Classical Music Recordings: A Cross-Version Study on Schubert’s Winterreise H Schreiber, C Weiß, M Müller Proceedings of the IEEE International Conference on Acoustics, Speech and …, 2020 | 16 | 2020 |
| Accelerating Index-Based Audio Identification H Schreiber, M Müller IEEE Transactions on Multimedia 16 (6), 1654-1664, 2014 | 15 | 2014 |
| A Re-ordering Strategy for Accelerating Index-based Audio Fingerprinting. H Schreiber, P Grosche, M Müller Proceedings of the International Society for Music Information Retrieval …, 2011 | 12 | 2011 |
| Towards Automatically Correcting Tapped Beat Annotations for Music Recordings J Driedger, H Schreiber, WB de Haas, M Müller Proceedings of the International Society for Music Information Retrieval …, 2019 | 11 | 2019 |
| Genre Ontology Learning: Comparing Curated with Crowd-Sourced Ontologies H Schreiber Proceedings of the International Society for Music Information Retrieval …, 2016 | 11 | 2016 |
| Exploiting Global Features for Tempo Octave Correction H Schreiber, M Müller Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International …, 2014 | 8 | 2014 |
| Performant Java programmieren:[Performance-Fallen erkennen und vermeiden] H Schreiber Addison-Wesley, 2002 | 8 | 2002 |
| CNN-based automatic musical key detection H Schreiber Music Information Retrieval Evaluation eXchange (MIREX), Suzhou, China, 2017 | 6* | 2017 |
Meinard MüllerInternational Audio Laboratories Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)Bestätigte E-Mail-Adresse bei audiolabs-erlangen.de
