Seguir
Hendrik Schreiber
Hendrik Schreiber
tagtraum industries incorporated
Dirección de correo verificada de tagtraum.com - Página principal
Título
Citado por
Citado por
Año
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
682018
Improving Genre Annotations for the Million Song Dataset
H Schreiber
Proceedings of the International Society for Music Information Retrieval …, 2015
622015
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
34*1999
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
312019
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
272019
Java server and servlets: building portable web applications
P Rossbach, H Schreiber
Addison-Wesley Longman Publishing Co., Inc., 2000
272000
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
252020
A Crowdsourced Experiment for Tempo Estimation of Electronic Dance Music
H Schreiber, M Müller
Proceedings of the International Society for Music Information Retrieval …, 2018
212018
The MediaEval 2018 AcousticBrainz genre task: Content-based music genre recognition from multiple sources
D Bogdanov, A Porter, J Urbano, H Schreiber
192018
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
192017
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
192017
Music Tempo Estimation: Are We Done Yet?
H Schreiber, J Urbano, M Müller
Transactions of the International Society for Music Information Retrieval …, 2020
182020
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
162020
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
142020
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
142011
Accelerating Index-Based Audio Identification
H Schreiber, M Müller
IEEE Transactions on Multimedia 16 (6), 1654-1664, 2014
132014
Genre Ontology Learning: Comparing Curated with Crowd-Sourced Ontologies
H Schreiber
Proceedings of the International Society for Music Information Retrieval …, 2016
112016
Exploiting Global Features for Tempo Octave Correction
H Schreiber, M Müller
Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International …, 2014
82014
Performant Java programmieren:[Performance-Fallen erkennen und vermeiden]
H Schreiber
Addison-Wesley, 2002
82002
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
72019
El sistema no puede realizar la operación en estos momentos. Inténtalo de nuevo más tarde.
Artículos 1–20