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2020 – today
- 2024
[c84]James Bolt, Johan Pauwels, György Fazekas:
Multi-Signal Informed Attention for Beat and Downbeat Detection. IS2 2024: 1-7- [i43]Chin-Yun Yu, Christopher Mitcheltree, Alistair Carson, Stefan Bilbao, Joshua D. Reiss, György Fazekas:
Differentiable All-pole Filters for Time-varying Audio Systems. CoRR abs/2404.07970 (2024) - [i42]Chin-Yun Yu, Johan Pauwels, György Fazekas:
Time-of-arrival Estimation and Phase Unwrapping of Head-related Transfer Functions With Integer Linear Programming. CoRR abs/2405.06804 (2024) - [i41]Chin-Yun Yu, György Fazekas:
Differentiable Time-Varying Linear Prediction in the Context of End-to-End Analysis-by-Synthesis. CoRR abs/2406.05128 (2024) - [i40]Yinghao Ma, Anders Øland, Anton Ragni, Bleiz Macsen Del Sette, Charalampos Saitis, Chris Donahue, Chenghua Lin, Christos Plachouras, Emmanouil Benetos, Elio Quinton, Elona Shatri, Fabio Morreale, Ge Zhang, György Fazekas, Gus Xia, Huan Zhang, Ilaria Manco, Jiawen Huang, Julien Guinot
, Liwei Lin, Luca Marinelli, Max W. Y. Lam, Megha Sharma, Qiuqiang Kong, Roger B. Dannenberg, Ruibin Yuan, Shangda Wu, Shih-Lun Wu, Shuqi Dai, Shun Lei, Shiyin Kang, Simon Dixon, Wenhu Chen, Wenhao Huang, Xingjian Du, Xingwei Qu, Xu Tan, Yizhi Li, Zeyue Tian, Zhiyong Wu, Zhizheng Wu, Ziyang Ma, Ziyu Wang:
Foundation Models for Music: A Survey. CoRR abs/2408.14340 (2024) - [i39]Elizabeth Wilson, György Fazekas, Geraint A. Wiggins:
Tidal MerzA: Combining affective modelling and autonomous code generation through Reinforcement Learning. CoRR abs/2409.07918 (2024) - 2023
- [j11]Luca Turchet, Mathieu Lagrange, Cristina Rottondi, György Fazekas, Nils Peters, Jan Østergaard, Frederic Font, Tom Bäckström, Carlo Fischione:
The Internet of Sounds: Convergent Trends, Insights, and Future Directions. IEEE Internet Things J. 10(13): 11264-11292 (2023) - [j10]Miguel Ceriani, Fabio Viola, Sasa Rudan, Francesco Antoniazzi, Mathieu Barthet, György Fazekas:
Semantic integration of audio content providers through the Audio Commons Ontology. J. Web Semant. 77: 100787 (2023) - [c83]James Bolt, György Fazekas:
Supervised Contrastive Learning For Musical Onset Detection. Audio Mostly Conference 2023: 130-135 - [c82]Sebastian Löbbers, Louise Thorpe, György Fazekas:
SketchSynth: Cross-Modal Control of Sound Synthesis. EvoMUSART@EvoStar 2023: 164-179 - [c81]Rodrigo Diaz, Ben Hayes, Charalampos Saitis, György Fazekas, Mark B. Sandler:
Rigid-Body Sound Synthesis with Differentiable Modal Resonators. ICASSP 2023: 1-5 - [c80]Ben Hayes, Charalampos Saitis, György Fazekas:
Sinusoidal Frequency Estimation by Gradient Descent. ICASSP 2023: 1-5 - [c79]Syed Rifat Mahmud Rafee, György Fazekas, Geraint A. Wiggins:
HIPI: A Hierarchical Performer Identification Model Based on Symbolic Representation of Music. ICASSP 2023: 1-5 - [c78]Chin-Yun Yu, Sung-Lin Yeh, György Fazekas, Hao Tang:
Conditioning and Sampling in Variational Diffusion Models for Speech Super-Resolution. ICASSP 2023: 1-5 - [c77]Ben Hayes, Charalampos Saitis, György Fazekas:
The Responsibility Problem in Neural Networks with Unordered Targets. Tiny Papers @ ICLR 2023 - [c76]Jingjing Tang, Geraint A. Wiggins, György Fazekas:
Pianist Identification Using Convolutional Neural Networks. IS2 2023: 1-6 - [c75]Luca Marinelli, György Fazekas, Charalampos Saitis:
Gender-Coded Sound: Analysing the Gendering of Music in Toy Commercials via Multi-Task Learning. ISMIR 2023: 166-173 - [c74]Chin-Yun Yu, György Fazekas:
Singing Voice Synthesis Using Differentiable LPC and Glottal-Flow-Inspired Wavetables. ISMIR 2023: 667-675 - [c73]Cyrus Vahidi, Shubhr Singh, Emmanouil Benetos, Huy Phan, Dan Stowell, György Fazekas, Mathieu Lagrange:
Perceptual Musical Similarity Metric Learning with Graph Neural Networks. WASPAA 2023: 1-5 - [i38]Cyrus Vahidi, Han Han, Changhong Wang, Mathieu Lagrange, György Fazekas, Vincent Lostanlen:
Mesostructures: Beyond Spectrogram Loss in Differentiable Time-Frequency Analysis. CoRR abs/2301.10183 (2023) - [i37]Sebastian Löbbers, Mathieu Barthet, György Fazekas:
AI as mediator between composers, sound designers, and creative media producers. CoRR abs/2303.01457 (2023) - [i36]Ben Hayes, Charalampos Saitis, György Fazekas:
The Responsibility Problem in Neural Networks with Unordered Targets. CoRR abs/2304.09499 (2023) - [i35]Chin-Yun Yu, György Fazekas:
Singing Voice Synthesis Using Differentiable LPC and Glottal-Flow-Inspired Wavetables. CoRR abs/2306.17252 (2023) - [i34]Ben Hayes, Jordie Shier, György Fazekas, Andrew P. McPherson, Charalampos Saitis:
A Review of Differentiable Digital Signal Processing for Music & Speech Synthesis. CoRR abs/2308.15422 (2023) - [i33]Soumya Sai Vanka, Maryam Safi, Jean-Baptiste Rolland, György Fazekas:
The Role of Communication and Reference Songs in the Mixing Process: Insights from Professional Mix Engineers. CoRR abs/2309.03404 (2023) - [i32]Jingjing Tang, Geraint A. Wiggins, György Fazekas:
Pianist Identification Using Convolutional Neural Networks. CoRR abs/2310.00699 (2023) - [i31]Jincheng Zhang, György Fazekas, Charalampos Saitis:
Fast Diffusion GAN Model for Symbolic Music Generation Controlled by Emotions. CoRR abs/2310.14040 (2023) - [i30]Jincheng Zhang, Jingjing Tang, Charalampos Saitis, György Fazekas:
Composer Style-specific Symbolic Music Generation Using Vector Quantized Discrete Diffusion Models. CoRR abs/2310.14044 (2023) - [i29]Chin-Yun Yu, Emilian Postolache, Emanuele Rodolà, György Fazekas:
Zero-Shot Duet Singing Voices Separation with Diffusion Models. CoRR abs/2311.07345 (2023) - [i28]Ilaria Manco, Benno Weck, Seungheon Doh, Minz Won, Yixiao Zhang, Dmitry Bogdanov, Yusong Wu, Ke Chen, Philip Tovstogan, Emmanouil Benetos, Elio Quinton, György Fazekas, Juhan Nam:
The Song Describer Dataset: a Corpus of Audio Captions for Music-and-Language Evaluation. CoRR abs/2311.10057 (2023) - 2022
- [j9]Luca Turchet, Paolo Bouquet, Andrea Molinari, György Fazekas:
The Smart Musical Instruments Ontology. J. Web Semant. 72: 100687 (2022) - [j8]Polina Proutskova, Daniel Wolff, György Fazekas, Klaus Frieler, Frank Höger, Olga Velichkina, Gabriel Solis, Tillman Weyde, Martin Pfleiderer, Hélène Camille Crayencour, Geoffroy Peeters, Simon Dixon:
The Jazz Ontology: A semantic model and large-scale RDF repositories for jazz. J. Web Semant. 74: 100735 (2022) - [c72]Yudong Zhao, György Fazekas, Mark Sandler:
Transfer Learning for Violinist Identification. EUSIPCO 2022: 239-243 - [c71]Ilaria Manco, Emmanouil Benetos, Elio Quinton, György Fazekas:
Learning Music Audio Representations Via Weak Language Supervision. ICASSP 2022: 456-460 - [c70]Yudong Zhao, György Fazekas, Mark B. Sandler:
Violinist Identification Using Note-Level Timbre Feature Distributions. ICASSP 2022: 601-605 - [i27]Sebastian Löbbers, György Fazekas:
Seeing Sounds, Hearing Shapes: a gamified study to evaluate sound-sketches. CoRR abs/2205.08866 (2022) - [i26]Ilaria Manco, Emmanouil Benetos, Elio Quinton, György Fazekas:
Contrastive Audio-Language Learning for Music. CoRR abs/2208.12208 (2022) - [i25]Ben Hayes, Charalampos Saitis, György Fazekas:
Sinusoidal Frequency Estimation by Gradient Descent. CoRR abs/2210.14476 (2022) - [i24]Rodrigo Diaz, Ben Hayes, Charalampos Saitis, György Fazekas, Mark Sandler:
Rigid-Body Sound Synthesis with Differentiable Modal Resonators. CoRR abs/2210.15306 (2022) - [i23]Chin-Yun Yu, Sung-Lin Yeh, György Fazekas, Hao Tang:
Conditioning and Sampling in Variational Diffusion Models for Speech Super-resolution. CoRR abs/2210.15793 (2022) - 2021
- [c69]Yudong Zhao, Changhong Wang, György Fazekas, Emmanouil Benetos, Mark B. Sandler:
Violinist identification based on vibrato features. EUSIPCO 2021: 381-385 - [c68]Ilaria Manco, Emmanouil Benetos, Elio Quinton, György Fazekas:
MusCaps: Generating Captions for Music Audio. IJCNN 2021: 1-8 - [c67]Cyrus Vahidi, Charalampos Saitis, György Fazekas:
A Modulation Front-End for Music Audio Tagging. IJCNN 2021: 1-7 - [c66]Ben Hayes, Charalampos Saitis, György Fazekas:
Neural Waveshaping Synthesis. ISMIR 2021: 254-261 - [i22]Beici Liang, György Fazekas, Mark B. Sandler:
Transfer Learning for Piano Sustain-Pedal Detection. CoRR abs/2103.13219 (2021) - [i21]Ilaria Manco, Emmanouil Benetos, Elio Quinton, György Fazekas:
MusCaps: Generating Captions for Music Audio. CoRR abs/2104.11984 (2021) - [i20]Cyrus Vahidi, Charalampos Saitis, György Fazekas:
A Modulation Front-End for Music Audio Tagging. CoRR abs/2105.11836 (2021) - [i19]Ben Hayes, Charalampos Saitis, György Fazekas:
Neural Waveshaping Synthesis. CoRR abs/2107.05050 (2021) - [i18]Sebastian Löbbers, Mathieu Barthet, György Fazekas:
Sketching sounds: an exploratory study on sound-shape associations. CoRR abs/2107.07360 (2021) - [i17]Elona Shatri, György Fazekas:
DoReMi: First glance at a universal OMR dataset. CoRR abs/2107.07786 (2021) - [i16]Syed Rifat Mahmud Rafee, György Fazekas, Geraint A. Wiggins:
Performer Identification From Symbolic Representation of Music Using Statistical Models. CoRR abs/2108.02576 (2021) - [i15]Ilaria Manco, Emmanouil Benetos, Elio Quinton, György Fazekas:
Learning music audio representations via weak language supervision. CoRR abs/2112.04214 (2021) - 2020
- [j7]Luca Turchet, György Fazekas, Mathieu Lagrange, Hossein Shokri Ghadikolaei, Carlo Fischione:
The Internet of Audio Things: State of the Art, Vision, and Challenges. IEEE Internet Things J. 7(10): 10233-10249 (2020) - [j6]Duncan A. H. Williams, Bruno Fazenda, Victoria Williamson, György Fazekas:
On Performance and Perceived Effort in Trail Runners Using Sensor Control to Generate Biosynchronous Music. Sensors 20(16): 4528 (2020) - [j5]Luca Turchet, Johan Pauwels, Carlo Fischione, György Fazekas:
Cloud-smart Musical Instrument Interactions: Querying a Large Music Collection with a Smart Guitar. ACM Trans. Internet Things 1(3): 15:1-15:29 (2020) - [j4]Luca Turchet, Francesco Antoniazzi, Fabio Viola, Fausto Giunchiglia, György Fazekas:
The Internet of Musical Things Ontology. J. Web Semant. 60: 100548 (2020) - [c65]Polina Proutskova, Anja Volk, Peyman Heydarian, György Fazekas:
From Music Ontology towards Ethno-Music-Ontology. ISMIR 2020: 923-931 - [c64]Duncan A. H. Williams, Bruno Fazenda, Victoria Williamson, György Fazekas:
Biophysiologically synchronous computer generated music improves performance and reduces perceived effort in trail runners. NIME 2020: 531-536 - [c63]Andrew Thompson, György Fazekas, Geraint A. Wiggins:
Poster: Programming Practices Among Interactive Audio Software Developers. VL/HCC 2020: 1-2 - [i14]Johan Pauwels, György Fazekas, Mark B. Sandler:
A Critical Look at the Applicability of Markov Logic Networks for Music Signal Analysis. CoRR abs/2001.06086 (2020) - [i13]Elona Shatri, György Fazekas:
Optical Music Recognition: State of the Art and Major Challenges. CoRR abs/2006.07885 (2020)
2010 – 2019
- 2019
- [c62]Gary Bromham, David Moffat, Mathieu Barthet, Anne Danielsen, György Fazekas:
The Impact of Audio Effects Processing on the Perception of Brightness and Warmth. Audio Mostly Conference 2019: 183-190 - [c61]Andrew Thompson, György Fazekas:
A Model-View-Update Framework for Interactive Web Audio Applications. Audio Mostly Conference 2019: 219-222 - [c60]Beici Liang, György Fazekas, Mark B. Sandler:
Piano Sustain-pedal Detection Using Convolutional Neural Networks. ICASSP 2019: 241-245 - [c59]Beici Liang, György Fazekas, Mark B. Sandler:
Transfer Learning for Piano Sustain-Pedal Detection. IJCNN 2019: 1-6 - [c58]Di Sheng, György Fazekas:
A Feature Learning Siamese Model for Intelligent Control of the Dynamic Range Compressor. IJCNN 2019: 1-8 - [i12]Di Sheng, György Fazekas:
A Feature Learning Siamese Model for Intelligent Control of the Dynamic Range Compressor. CoRR abs/1905.01022 (2019) - 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. Emerg. Top. Comput. Intell. 2(2): 139-149 (2018) - [c57]Anna Xambó, Johan Pauwels, Gerard Roma, Mathieu Barthet, György Fazekas:
Jam with Jamendo: Querying a Large Music Collection by Chords from a Learner's Perspective. Audio Mostly Conference 2018: 30:1-30:7 - [c56]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 - [c55]Beici Liang, György Fazekas, Mark B. Sandler:
Piano Legato-Pedal Onset Detection Based on a Sympathetic Resonance Measure. EUSIPCO 2018: 2484-2488 - [c54]Luca Turchet, Fabio Viola, György Fazekas, Mathieu Barthet:
Towards a Semantic Architecture for the Internet of Musical Things. FRUCT 2018: 382-390 - [c53]Fabio Viola, Luca Turchet, Francesco Antoniazzi, György Fazekas:
C Minor: a Semantic Publish/Subscribe Broker for the Internet of Musical Things. FRUCT 2018: 405-415 - [c52]Di Sheng, György Fazekas:
Feature Design Using Audio Decomposition for Intelligent Control of the Dynamic Range Compressor. ICASSP 2018: 621-625 - [c51]Anna Xambó, Gerard Roma, Alexander Lerch, Mathieu Barthet, György Fazekas:
Live Repurposing of Sounds: MIR Explorations with Personal and Crowdsourced Databases. NIME 2018: 364-369 - [c50]Miguel Ceriani, György Fazekas:
Audio Commons Ontology: A Data Model for an Audio Content Ecosystem. ISWC (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 - [e3]Sean Bechhofer, György Fazekas, Kevin R. Page:
Proceedings of the 1st International Workshop on Semantic Applications for Audio and Music, SAAM@ISWC 2018, Monterey, CA, USA, October 9, 2018. ACM 2018, ISBN 978-1-4503-6495-9 [contents] - 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 P. 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. ISWC (2) 2017: 29-37 - [e2]György Fazekas, Mathieu Barthet, Tony Stockman:
Proceedings of the 12th International Audio Mostly Conference on Augmented and Participatory Sound and Music Experiences, London, United Kingdom, August 23 - 26, 2017. ACM 2017, ISBN 978-1-4503-5373-1 [contents] - [i11]Keunwoo Choi, György Fazekas, Mark B. Sandler, Kyunghyun Cho:
Transfer learning for music classification and regression tasks. CoRR abs/1703.09179 (2017) - [i10]Keunwoo Choi, György Fazekas, Kyunghyun Cho, Mark B. Sandler:
On the Robustness of Deep Convolutional Neural Networks for Music Classification. CoRR abs/1706.02361 (2017) - [i9]Keunwoo Choi, György Fazekas, Kyunghyun Cho, Mark B. Sandler:
A Comparison on Audio Signal Preprocessing Methods for Deep Neural Networks on Music Tagging. CoRR abs/1709.01922 (2017) - [i8]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. Affect. Comput. 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]Alo Allik, György Fazekas, Mark B. Sandler:
An Ontology for Audio Features. ISMIR 2016: 73-79 - [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. ISWC (2) 2016: 3-11 - [c30]Alo Allik, Mariano Mora-Mcginity, György Fazekas, Mark B. Sandler:
MusicWeb: Music Discovery with Open Linked Semantic Metadata. ISWC (Posters & Demos) 2016 - [c29]Thomas Wilmering, György Fazekas, Mark B. Sandler:
AUFX-O: Novel Methods for the Representation of Audio Processing Workflows. ISWC (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 - [i7]Keunwoo Choi, György Fazekas, Mark B. Sandler:
Text-based LSTM networks for Automatic Music Composition. CoRR abs/1604.05358 (2016) - [i6]Keunwoo Choi, György Fazekas, Mark B. Sandler:
Automatic tagging using deep convolutional neural networks. CoRR abs/1606.00298 (2016) - [i5]Keunwoo Choi, György Fazekas, Mark B. Sandler:
Towards Playlist Generation Algorithms Using RNNs Trained on Within-Track Transitions. CoRR abs/1606.02096 (2016) - [i4]Keunwoo Choi, György Fazekas, Mark B. Sandler:
Explaining Deep Convolutional Neural Networks on Music Classification. CoRR abs/1607.02444 (2016) - [i3]Keunwoo Choi, György Fazekas, Mark B. Sandler:
Towards Music Captioning: Generating Music Playlist Descriptions. CoRR abs/1608.04868 (2016) - [i2]Keunwoo Choi, György Fazekas, Mark B. Sandler, Kyunghyun Cho:
Convolutional Recurrent Neural Networks for Music Classification. CoRR abs/1609.04243 (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 - [i1]Keunwoo Choi, György Fazekas, Mark B. Sandler:
Understanding Music Playlists. CoRR abs/1511.07004 (2015) - 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. ISWC (Posters & Demos) 2014: 209-212 - [e1]Christian Dittmar, György Fazekas, Sebastian Ewert:
AES International Conference on Semantic Audio 2014, London, UK, January 27-29, 2014. Audio Engineering Society 2014, ISBN 978-0-937803-96-7 [contents] - 2013
- [j1]Sefki Kolozali, Mathieu Barthet, György Fazekas, Mark B. Sandler:
Automatic Ontology Generation for Musical Instruments Based on Audio Analysis. IEEE Trans. Speech Audio Process. 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]Thomas Wilmering, György Fazekas, Mark B. Sandler:
The Audio Effects Ontology. ISMIR 2013: 215-220 - [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]György Fazekas, Mark B. Sandler:
The Studio Ontology Framework. ISMIR 2011: 471-476 - [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
[c81] [c72] [c70] [i24] [c69] [i22] [i14] [c60] [c59] [j3] [c56] [c55] [c48] [c46] [c45] [c44] [c42] [i11] [i10] [i9] [i8] [j2] [c40] [c38] [c37] [c36] [c33] [c32] [c31] [c30] [c29] [c28] [i7] [i6] [i5] [i4] [i3] [i2] [c27] [c26] [i1] [c24] [c23] [c22] [c21] [j1] [c19] [c18] [c17] [c16] [c15] [c14] [c13] [c12] [c11] [c10] [c9] [c8] [c7] [c5] [c4] [c3] [c2] [c1]
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