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Semantic ScholarGyörgy Fazekas
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2010 – today
- 2018
[j3]Keunwoo Choi, György Fazekas, Kyunghyun Cho, Mark B. Sandler:
The Effects of Noisy Labels on Deep Convolutional Neural Networks for Music Tagging. IEEE Trans. Emerging Topics in Comput. Intellig. 2(2): 139-149 (2018)- [c53]Keunwoo Choi, György Fazekas, Mark B. Sandler, Kyunghyun Cho:
A Comparison of Audio Signal Preprocessing Methods for Deep Neural Networks on Music Tagging. EUSIPCO 2018: 1870-1874 - [c52]Beici Liang, György Fazekas, Mark B. Sandler:
Piano Legato-Pedal Onset Detection Based on a Sympathetic Resonance Measure. EUSIPCO 2018: 2484-2488 - [c51]Di Sheng, György Fazekas:
Feature Design Using Audio Decomposition for Intelligent Control of the Dynamic Range Compressor. ICASSP 2018: 621-625 - [c50]Miguel Ceriani, György Fazekas:
Audio Commons Ontology: A Data Model for an Audio Content Ecosystem. International Semantic Web Conference (2) 2018: 20-35 - [c49]Fabio Viola, Ariane Stolfi, Alessia Milo, Miguel Ceriani, Mathieu Barthet, György Fazekas:
Playsound.space: enhancing a live music performance tool with semantic recommendations. SAAM@ISWC 2018: 46-53 - 2017
- [c48]Beici Liang, György Fazekas, Mark B. Sandler:
Recognition of Piano Pedalling Techniques Using Gesture Data. Audio Mostly Conference 2017: 9:1-9:5 - [c47]Lucia Marengo, György Fazekas, Anastasios Tombros:
The Interaction of Casual Users with Digital Collections of Visual Art. An Exploratory Study of the WikiArt Website. HCI (30) 2017: 583-590 - [c46]Keunwoo Choi, György Fazekas, Mark B. Sandler, Kyunghyun Cho:
Convolutional recurrent neural networks for music classification. ICASSP 2017: 2392-2396 - [c45]Keunwoo Choi, György Fazekas, Mark B. Sandler, Kyunghyun Cho:
Transfer Learning for Music Classification and Regression Tasks. ISMIR 2017: 141-149 - [c44]Johan Pauwels, Ken O'Hanlon, György Fazekas, Mark B. Sandler:
Confidence Measures and Their Applications in Music Labelling Systems Based on Hidden Markov Models. ISMIR 2017: 279-285 - [c43]Kevin R. Page, Sean Bechhofer, György Fazekas, David M. Weigl, Thomas Wilmering:
Realising a Layered Digital Library: Exploration and Analysis of the Live Music Archive through Linked Data. JCDL 2017: 89-98 - [c42]Beici Liang, György Fazekas, Andrew McPherson, Mark B. Sandler:
Piano pedaller: a measurement system for classification and visualisation of piano pedalling techniques. NIME 2017: 325-329 - [c41]Sean Bechhofer, Kevin R. Page, David M. Weigl, György Fazekas, Thomas Wilmering:
Linked Data Publication of Live Music Archives and Analyses. International Semantic Web Conference (2) 2017: 29-37 - [i2]Keunwoo Choi, György Fazekas, Mark B. Sandler, Kyunghyun Cho:
Transfer learning for music classification and regression tasks. CoRR abs/1703.09179 (2017) - [i1]Keunwoo Choi, György Fazekas, Kyunghyun Cho, Mark B. Sandler:
A Tutorial on Deep Learning for Music Information Retrieval. CoRR abs/1709.04396 (2017) - 2016
- [j2]Pasi Saari, György Fazekas, Tuomas Eerola, Mathieu Barthet, Olivier Lartillot, Mark B. Sandler:
Genre-Adaptive Semantic Computing and Audio-Based Modelling for Music Mood Annotation. IEEE Trans. Affective Computing 7(2): 122-135 (2016) - [c40]Florian Thalmann, György Fazekas, Geraint A. Wiggins, Mark B. Sandler:
Creating, Visualizing, and Analyzing Dynamic Music Objects in the Browser with the Dymo Designer. Audio Mostly Conference 2016: 39-46 - [c39]Paulo Chiliguano, György Fazekas:
Hybrid music recommender using content-based and social information. ICASSP 2016: 2618-2622 - [c38]
- [c37]Michele Buccoli, Massimiliano Zanoni, György Fazekas, Augusto Sarti, Mark B. Sandler:
A Higher-Dimensional Expansion of Affective Norms for English Terms for Music Tagging. ISMIR 2016: 316-322 - [c36]Keunwoo Choi, György Fazekas, Mark B. Sandler:
Automatic Tagging Using Deep Convolutional Neural Networks. ISMIR 2016: 805-811 - [c35]Ryan Stables, Brecht De Man, Sean Enderby, Joshua D. Reiss, György Fazekas, Thomas Wilmering:
Semantic Description of Timbral Transformations in Music Production. ACM Multimedia 2016: 337-341 - [c34]Damir Juric, György Fazekas:
Knowledge Extraction from Audio Content Service Providers' API Descriptions. MTSR 2016: 55-66 - [c33]Mariano Mora-Mcginity, Alo Allik, György Fazekas, Mark B. Sandler:
MusicWeb: Music Discovery with Open Linked Semantic Metadata. MTSR 2016: 291-296 - [c32]Florian Thalmann, Alfonso Pérez Carrillo, György Fazekas, Geraint A. Wiggins, Mark B. Sandler:
The Mobile Audio Ontology: Experiencing Dynamic Music Objects on Mobile Devices. ICSC 2016: 47-54 - [c31]Alo Allik, György Fazekas, Mark B. Sandler:
Ontological Representation of Audio Features. International Semantic Web Conference (2) 2016: 3-11 - [c30]Alo Allik, Mariano Mora-Mcginity, György Fazekas, Mark B. Sandler:
MusicWeb: Music Discovery with Open Linked Semantic Metadata. International Semantic Web Conference (Posters & Demos) 2016 - [c29]Thomas Wilmering, György Fazekas, Mark B. Sandler:
AUFX-O: Novel Methods for the Representation of Audio Processing Workflows. International Semantic Web Conference (2) 2016: 229-237 - [c28]Keunwoo Choi, György Fazekas, Mark B. Sandler:
Towards Playlist Generation Algorithms Using RNNs Trained on Within-Track Transitions. UMAP (Extended Proceedings) 2016 - 2015
- [c27]Mathieu Barthet, György Fazekas, Alo Allik, Mark B. Sandler:
Moodplay: an interactive mood-based musical experience. Audio Mostly Conference 2015: 3:1-3:8 - [c26]Mi Tian, György Fazekas, Dawn A. A. Black, Mark B. Sandler:
On the use of the tempogram to describe audio content and its application to Music structural segmentation. ICASSP 2015: 419-423 - [c25]Thomas Wilmering, Kevin R. Page, György Fazekas, Simon Dixon, Sean Bechhofer:
Automating Annotation of Media with Linked Data Workflows. WWW (Companion Volume) 2015: 737-738 - 2014
- [c24]Mi Tian, György Fazekas, Dawn A. A. Black, Mark B. Sandler:
Design And Evaluation of Onset Detectors using Different Fusion Policies. ISMIR 2014: 631-636 - [c23]Chris Baume, György Fazekas, Mathieu Barthet, David Marston, Mark B. Sandler:
Selection of Audio Features for Music Emotion Recognition Using Production Music. Semantic Audio 2014 - [c22]Sefki Kolozali, György Fazekas, Mathieu Barthet, Mark B. Sandler:
A Framework for Automatic Ontology Generation Based on Semantic Audio Analysis. Semantic Audio 2014 - [c21]Ting Lou, Mathieu Barthet, György Fazekas, Mark B. Sandler:
Evaluation and Improvement of the Mood Conductor Interactive System. Semantic Audio 2014 - [c20]Frederic Font, Sergio Oramas, György Fazekas, Xavier Serra:
Extending Tagging Ontologies with Domain Specific Knowledge. International Semantic Web Conference (Posters & Demos) 2014: 209-212 - 2013
- [j1]Sefki Kolozali, Mathieu Barthet, György Fazekas, Mark B. Sandler:
Automatic Ontology Generation for Musical Instruments Based on Audio Analysis. IEEE Trans. Audio, Speech & Language Processing 21(10): 2207-2220 (2013) - [c19]György Fazekas, Mathieu Barthet, Mark B. Sandler:
Mood Conductor: Emotion-Driven Interactive Music Performance. ACII 2013: 726 - [c18]György Fazekas, Mathieu Barthet, Mark B. Sandler:
Novel Methods in Facilitating Audience and Performer Interaction Using the Mood Conductor Framework. CMMR 2013: 122-147 - [c17]Mi Tian, György Fazekas, Dawn A. A. Black, Mark B. Sandler:
Towards the Representation of Chinese Traditional Music: A State of the Art Review of Music Metadata Standards. Dublin Core Conference 2013: 71-81 - [c16]Alo Allik, György Fazekas, Simon Dixon, Mark B. Sandler:
Facilitating Music Information Research with Shared Open Vocabularies. ESWC (Satellite Events) 2013: 178-183 - [c15]Alo Allik, György Fazekas, Simon Dixon, Mark B. Sandler:
A Shared Vocabulary for Audio Features. ESWC (Satellite Events) 2013: 285-286 - [c14]György Fazekas, Mathieu Barthet, Mark B. Sandler:
Demo paper: The BBC Desktop Jukebox music recommendation system: A large scale trial with professional users. ICME Workshops 2013: 1-2 - [c13]Pasi Saari, Mathieu Barthet, György Fazekas, Tuomas Eerola, Mark B. Sandler:
Semantic models of musical mood: Comparison between crowd-sourced and curated editorial tags. ICME Workshops 2013: 1-6 - [c12]Pasi Saari, Tuomas Eerola, György Fazekas, Mathieu Barthet, Olivier Lartillot, Mark B. Sandler:
The Role of Audio and Tags in Music Mood Prediction: A Study Using Semantic Layer Projection. ISMIR 2013: 201-206 - [c11]
- [c10]Katerina Kosta, Yading Song, György Fazekas, Mark B. Sandler:
A Study of Cultural Dependence of Perceived Mood in Greek Music. ISMIR 2013: 317-322 - [c9]Mathieu Barthet, David Marston, Chris Baume, György Fazekas, Mark B. Sandler:
Design and Evaluation of Semantic Mood Models for Music Recommendation using Editorial Tags. ISMIR 2013: 421-426 - [c8]György Fazekas, Mark B. Sandler:
Describing audio production workflows on the semantic web. WIAMIS 2013: 1-4 - 2012
- [b1]György Fazekas:
Semantic audio analysis utilities and applications. Queen Mary University of London, UK 2012 - [c7]Mathieu Barthet, György Fazekas, Mark B. Sandler:
Music Emotion Recognition: From Content- to Context-Based Models. CMMR 2012: 228-252 - [c6]Michael Terrell, György Fazekas, Andrew Simpson, Jordan B. L. Smith, Simon Dixon:
Listening Level Changes Music Similarity. ISMIR 2012: 487-492 - 2011
- [c5]Sefki Kolozali, Mathieu Barthet, György Fazekas, Mark B. Sandler:
Knowledge Representation Issues in Musical Instrument Ontology Design. ISMIR 2011: 465-470 - [c4]
- [c3]György Fazekas, Thomas Wilmering, Mark B. Sandler:
A Knowledge Representation Framework for Context-Dependent Audio Processing. Semantic Audio 2011 - 2010
- [c2]Sefki Kolozali, Mathieu Barthet, György Fazekas, Mark B. Sandler:
Towards the Automatic Generation of a Semantic Web Ontology for Musical Instruments. SAMT 2010: 186-187
2000 – 2009
- 2009
- [c1]Dan Tidhar, György Fazekas, Sefki Kolozali, Mark B. Sandler:
Publishing Music Similarity Features on the Semantic Web. ISMIR 2009: 447-452
Coauthor Index
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