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Hendrik Schreiber
Hendrik Schreiber
tagtraum industries incorporated
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Titel
Zitiert von
Zitiert von
Jahr
Improving Genre Annotations for the Million Song Dataset
H Schreiber
Proceedings of the International Society for Music Information Retrieval …, 2015
562015
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
492018
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
37*1999
Java server and servlets: building portable web applications
P Rossbach, H Schreiber
Addison-Wesley Longman Publishing Co., Inc., 2000
292000
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
232019
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
232019
The MediaEval 2018 AcousticBrainz genre task: Content-based music genre recognition from multiple sources
D Bogdanov, A Porter, J Urbano, H Schreiber
182018
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
182017
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
172017
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
162020
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
152011
A Crowdsourced Experiment for Tempo Estimation of Electronic Dance Music
H Schreiber, M Müller
Proceedings of the International Society for Music Information Retrieval …, 2018
142018
Accelerating Index-Based Audio Identification
H Schreiber, M Müller
IEEE Transactions on Multimedia 16 (6), 1654-1664, 2014
142014
Genre Ontology Learning: Comparing Curated with Crowd-Sourced Ontologies
H Schreiber
Proceedings of the International Society for Music Information Retrieval …, 2016
132016
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
122020
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
112020
Music Tempo Estimation: Are We Done Yet?
H Schreiber, J Urbano, M Müller
Transactions of the International Society for Music Information Retrieval …, 2020
102020
Exploiting Global Features for Tempo Octave Correction
H Schreiber, M Müller
Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International …, 2014
92014
Performant Java programmieren:[Performance-Fallen erkennen und vermeiden]
H Schreiber
Addison-Wesley, 2002
92002
CNN-based automatic musical key detection
H Schreiber
Music Information Retrieval Evaluation eXchange (MIREX), Suzhou, China, 2017
7*2017
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