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ORCIDGerhard Widmer
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- affiliation: Johannes Kepler University of Linz, Austria
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2020 – today
- 2024
[j52]Hamid Eghbal-zadeh
, Werner Zellinger, Maura Pintor, Kathrin Grosse, Khaled Koutini, Bernhard Alois Moser, Battista Biggio, Gerhard Widmer:
Rethinking data augmentation for adversarial robustness. Inf. Sci. 654: 119838 (2024)- [j51]Florian Schmid, Khaled Koutini, Gerhard Widmer:
Dynamic Convolutional Neural Networks as Efficient Pre-Trained Audio Models. IEEE ACM Trans. Audio Speech Lang. Process. 32: 2227-2241 (2024) - [c223]Paul Primus, Gerhard Widmer:
Fusing Audio and Metadata Embeddings Improves Language-Based Audio Retrieval. EUSIPCO 2024: 321-325 - [c222]Emmanouil Karystinaios, Francesco Foscarin, Gerhard Widmer:
Perception-Inspired Graph Convolution for Music Understanding Tasks. IJCAI 2024: 7681-7689 - [i130]Silvan David Peter, Carlos Eduardo Cancino Chacón, Emmanouil Karystinaios, Gerhard Widmer:
Sounding Out Reconstruction Error-Based Evaluation of Generative Models of Expressive Performance. CoRR abs/2401.00471 (2024) - [i129]Silvan David Peter, Shreyan Chowdhury, Carlos Eduardo Cancino Chacón, Gerhard Widmer:
Are we describing the same sound? An analysis of word embedding spaces of expressive piano performance. CoRR abs/2401.02979 (2024) - [i128]Shreyan Chowdhury, Gerhard Widmer:
Expressivity-aware Music Performance Retrieval using Mid-level Perceptual Features and Emotion Word Embeddings. CoRR abs/2401.14826 (2024) - [i127]Emmanouil Karystinaios, Francesco Foscarin, Gerhard Widmer:
Perception-Inspired Graph Convolution for Music Understanding Tasks. CoRR abs/2405.09224 (2024) - [i126]Emmanouil Karystinaios, Francesco Foscarin, Gerhard Widmer:
SMUG-Explain: A Framework for Symbolic Music Graph Explanations. CoRR abs/2405.09241 (2024) - [i125]Florian Schmid, Paul Primus, Toni Heittola, Annamaria Mesaros, Irene Martín-Morató, Khaled Koutini, Gerhard Widmer:
Data-Efficient Low-Complexity Acoustic Scene Classification in the DCASE 2024 Challenge. CoRR abs/2405.10018 (2024) - [i124]Patricia Hu, Lukás Samuel Marták, Carlos Cancino Chacón, Gerhard Widmer:
Towards Musically Informed Evaluation of Piano Transcription Models. CoRR abs/2406.08454 (2024) - [i123]Paul Primus, Gerhard Widmer:
Fusing Audio and Metadata Embeddings Improves Language-based Audio Retrieval. CoRR abs/2406.15897 (2024) - [i122]Emmanouil Karystinaios, Gerhard Widmer:
GraphMuse: A Library for Symbolic Music Graph Processing. CoRR abs/2407.12671 (2024) - [i121]Francesco Foscarin, Emmanouil Karystinaios, Eita Nakamura, Gerhard Widmer:
Cluster and Separate: a GNN Approach to Voice and Staff Prediction for Score Engraving. CoRR abs/2407.21030 (2024) - [i120]Francesco Foscarin, Jan Schlüter, Gerhard Widmer:
Beat this! Accurate beat tracking without DBN postprocessing. CoRR abs/2407.21658 (2024) - [i119]Florian Schmid, Paul Primus, Tobias Morocutti, Jonathan Greif, Gerhard Widmer:
Improving Audio Spectrogram Transformers for Sound Event Detection Through Multi-Stage Training. CoRR abs/2408.00791 (2024) - [i118]Silvan David Peter, Gerhard Widmer:
TheGlueNote: Learned Representations for Robust and Flexible Note Alignment. CoRR abs/2408.04309 (2024) - [i117]Lukás Samuel Marták, Patricia Hu, Gerhard Widmer:
Quantifying the Corpus Bias Problem in Automatic Music Transcription Systems. CoRR abs/2408.04737 (2024) - [i116]Mathias Rose Bjare, Stefan Lattner, Gerhard Widmer:
Controlling Surprisal in Music Generation via Information Content Curve Matching. CoRR abs/2408.06022 (2024) - [i115]Paul Primus, Florian Schmid, Gerhard Widmer:
Estimated Audio-Caption Correspondences Improve Language-Based Audio Retrieval. CoRR abs/2408.11641 (2024) - [i114]Florian Schmid, Tobias Morocutti, Francesco Foscarin, Jan Schlüter, Paul Primus, Gerhard Widmer:
Effective Pre-Training of Audio Transformers for Sound Event Detection. CoRR abs/2409.09546 (2024) - 2023
- [j50]Katharina Hoedt, Verena Praher, Arthur Flexer, Gerhard Widmer:
Constructing adversarial examples to investigate the plausibility of explanations in deep audio and image classifiers. Neural Comput. Appl. 35(14): 10011-10029 (2023) - [j49]Silvan David Peter, Carlos Eduardo Cancino Chacón, Francesco Foscarin, Andrew McLeod, Florian Henkel, Emmanouil Karystinaios, Gerhard Widmer:
Automatic Note-Level Score-to-Performance Alignments in the ASAP Dataset. Trans. Int. Soc. Music. Inf. Retr. 6(1): 27-42 (2023) - [c221]Silvan David Peter, Carlos Eduardo Cancino Chacón, Emmanouil Karystinaios, Gerhard Widmer:
Sounding Out Reconstruction Error-Based Evaluation of Generative Models of Expressive Performance. DLfM 2023: 58-66 - [c220]Tobias Morocutti, Florian Schmid, Khaled Koutini, Gerhard Widmer:
Device-Robust Acoustic Scene Classification via Impulse Response Augmentation. EUSIPCO 2023: 176-180 - [c219]Shahed Masoudian, Khaled Koutini, Markus Schedl, Gerhard Widmer, Navid Rekabsaz:
Domain Information Control at Inference Time for Acoustic Scene Classification. EUSIPCO 2023: 181-185 - [c218]Paul Primus, Gerhard Widmer:
On Frequency-Wise Normalizations for Better Recording Device Generalization in Audio Spectrogram Transformers. EUSIPCO 2023: 391-395 - [c217]Florian Schmid, Khaled Koutini, Gerhard Widmer:
Low-Complexity Audio Embedding Extractors. EUSIPCO 2023: 451-455 - [c216]Silvan David Peter, Shreyan Chowdhury, Carlos Eduardo Cancino Chacón, Gerhard Widmer:
Are we describing the same sound? An analysis of word embedding spaces of expressive piano performance. FIRE 2023: 58-66 - [c215]Shreyan Chowdhury, Gerhard Widmer:
Expressivity-aware Music Performance Retrieval using Mid-level Perceptual Features and Emotion Word Embeddings. FIRE 2023: 73-77 - [c214]Ali Nikrang, Maarten Grachten, Martin Gasser, Harald Frostel, Gerhard Widmer, Tom Collins:
Music Visualisation and Its Short-Term Effect on Appraisal Skills. HCI (50) 2023: 112-130 - [c213]Florian Schmid, Khaled Koutini, Gerhard Widmer:
Efficient Large-Scale Audio Tagging Via Transformer-to-CNN Knowledge Distillation. ICASSP 2023: 1-5 - [c212]Emmanouil Karystinaios, Francesco Foscarin, Gerhard Widmer:
Musical Voice Separation as Link Prediction: Modeling a Musical Perception Task as a Multi-Trajectory Tracking Problem. IJCAI 2023: 3866-3874 - [c211]Carlos Cancino Chacón, Silvan Peter, Patricia Hu, Emmanouil Karystinaios, Florian Henkel, Francesco Foscarin, Gerhard Widmer:
The ACCompanion: Combining Reactivity, Robustness, and Musical Expressivity in an Automatic Piano Accompanist. IJCAI 2023: 5779-5787 - [c210]Matthias Plasser, Silvan Peter, Gerhard Widmer:
Discrete Diffusion Probabilistic Models for Symbolic Music Generation. IJCAI 2023: 5842-5850 - [c209]Patricia Hu, Gerhard Widmer:
The Batik-Plays-Mozart Corpus: Linking Performance to Score to Musicological Annotations. ISMIR 2023: 297-303 - [c208]Francesco Foscarin, Daniel Harasim, Gerhard Widmer:
Predicting Music Hierarchies With a Graph-Based Neural Decoder. ISMIR 2023: 425-432 - [c207]Emmanouil Karystinaios, Gerhard Widmer:
Roman Numeral Analysis With Graph Neural Networks: Onset-Wise Predictions From Note-Wise Features. ISMIR 2023: 597-604 - [c206]Luís Carvalho, Gerhard Widmer:
Passage Summarization With Recurrent Models for Audio - Sheet Music Retrieval. ISMIR 2023: 700-707 - [c205]Mathias Rose Bjare, Stefan Lattner, Gerhard Widmer:
Exploring Sampling Techniques for Generating Melodies With a Transformer Language Model. ISMIR 2023: 810-816 - [c204]Huan Zhang, Emmanouil Karystinaios, Simon Dixon, Gerhard Widmer, Carlos Eduardo Cancino Chacón:
Symbolic Music Representations for Classification Tasks: A Systematic Evaluation. ISMIR 2023: 848-858 - [c203]Luís Carvalho, Gerhard Widmer:
Towards Robust and Truly Large-Scale Audio-Sheet Music Retrieval. MIPR 2023: 1-6 - [c202]Luís Carvalho, Tobias Washüttl, Gerhard Widmer:
Self-Supervised Contrastive Learning for Robust Audio-Sheet Music Retrieval Systems. MMSys 2023: 239-248 - [i113]Shreyan Chowdhury, Gerhard Widmer:
Decoding and Visualising Intended Emotion in an Expressive Piano Performance. CoRR abs/2303.01875 (2023) - [i112]Florian Schmid, Khaled Koutini, Gerhard Widmer:
Low-Complexity Audio Embedding Extractors. CoRR abs/2303.01879 (2023) - [i111]Carlos Cancino Chacón, Silvan Peter, Patricia Hu, Emmanouil Karystinaios, Florian Henkel, Francesco Foscarin, Nimrod Varga, Gerhard Widmer:
The ACCompanion: Combining Reactivity, Robustness, and Musical Expressivity in an Automatic Piano Accompanist. CoRR abs/2304.12939 (2023) - [i110]Emmanouil Karystinaios, Francesco Foscarin, Gerhard Widmer:
Musical Voice Separation as Link Prediction: Modeling a Musical Perception Task as a Multi-Trajectory Tracking Problem. CoRR abs/2304.14848 (2023) - [i109]Tobias Morocutti, Florian Schmid, Khaled Koutini, Gerhard Widmer:
Device-Robust Acoustic Scene Classification via Impulse Response Augmentation. CoRR abs/2305.07499 (2023) - [i108]Matthias Plasser, Silvan Peter, Gerhard Widmer:
Discrete Diffusion Probabilistic Models for Symbolic Music Generation. CoRR abs/2305.09489 (2023) - [i107]Shahed Masoudian, Khaled Koutini, Markus Schedl, Gerhard Widmer, Navid Rekabsaz:
Domain Information Control at Inference Time for Acoustic Scene Classification. CoRR abs/2306.08010 (2023) - [i106]Paul Primus, Gerhard Widmer:
On Frequency-Wise Normalizations for Better Recording Device Generalization in Audio Spectrogram Transformers. CoRR abs/2306.11764 (2023) - [i105]Francesco Foscarin, Daniel Harasim, Gerhard Widmer:
Predicting Music Hierarchies with a Graph-Based Neural Decoder. CoRR abs/2306.16955 (2023) - [i104]Emmanouil Karystinaios, Gerhard Widmer:
Roman Numeral Analysis with Graph Neural Networks: Onset-wise Predictions from Note-wise Features. CoRR abs/2307.03544 (2023) - [i103]Paul Primus, Khaled Koutini, Gerhard Widmer:
Advancing Natural-Language Based Audio Retrieval with PaSST and Large Audio-Caption Data Sets. CoRR abs/2308.04258 (2023) - [i102]Mathias Rose Bjare, Stefan Lattner, Gerhard Widmer:
Exploring Sampling Techniques for Generating Melodies with a Transformer Language Model. CoRR abs/2308.09454 (2023) - [i101]Patricia Hu, Gerhard Widmer:
The Batik-plays-Mozart Corpus: Linking Performance to Score to Musicological Annotations. CoRR abs/2309.02399 (2023) - [i100]Huan Zhang, Emmanouil Karystinaios, Simon Dixon, Gerhard Widmer, Carlos Eduardo Cancino Chacón:
Symbolic Music Representations for Classification Tasks: A Systematic Evaluation. CoRR abs/2309.02567 (2023) - [i99]Luís Carvalho, Gerhard Widmer:
Passage Summarization with Recurrent Models for Audio-Sheet Music Retrieval. CoRR abs/2309.12111 (2023) - [i98]Luís Carvalho, Tobias Washüttl, Gerhard Widmer:
Self-Supervised Contrastive Learning for Robust Audio-Sheet Music Retrieval Systems. CoRR abs/2309.12134 (2023) - [i97]Luís Carvalho, Gerhard Widmer:
Towards Robust and Truly Large-Scale Audio-Sheet Music Retrieval. CoRR abs/2309.12158 (2023) - [i96]Emmanouil Karystinaios, Francesco Foscarin, Florent Jacquemard, Masahiko Sakai, Satoshi Tojo, Gerhard Widmer:
8+8=4: Formalizing Time Units to Handle Symbolic Music Durations. CoRR abs/2310.14952 (2023) - [i95]Florian Schmid, Khaled Koutini, Gerhard Widmer:
Dynamic Convolutional Neural Networks as Efficient Pre-trained Audio Models. CoRR abs/2310.15648 (2023) - 2022
- [j48]Mathias Rose Bjare, Stefan Lattner, Gerhard Widmer:
Differentiable Short-Term Models for Efficient Online Learning and Prediction in Monophonic Music. Trans. Int. Soc. Music. Inf. Retr. 5(1): 190 (2022) - [c201]Paul Primus, Gerhard Widmer:
Improving Natural-Language-Based Audio Retrieval with Transfer Learning and Audio & Text Augmentations. DCASE 2022 - [c200]Florian Schmid, Shahed Masoudian, Khaled Koutini, Gerhard Widmer:
Knowledge Distillation from Transformers for Low-Complexity Acoustic Scene Classification. DCASE 2022 - [c199]Paul Primus, Gerhard Widmer:
Improved Zero-Shot Audio Tagging & Classification with Patchout Spectrogram Transformers. EUSIPCO 2022: 410-413 - [c198]Khaled Koutini, Jan Schlüter, Hamid Eghbal-zadeh, Gerhard Widmer:
Efficient Training of Audio Transformers with Patchout. INTERSPEECH 2022: 2753-2757 - [c197]Francesco Foscarin, Katharina Hoedt, Verena Praher, Arthur Flexer, Gerhard Widmer:
Concept-Based Techniques for "Musicologist-Friendly" Explanations in Deep Music Classifiers. ISMIR 2022: 876-883 - [c196]Emmanouil Karystinaios, Gerhard Widmer:
Cadence Detection in Symbolic Classical Music using Graph Neural Networks.. ISMIR 2022: 917-924 - [i94]Katharina Hoedt, Arthur Flexer, Gerhard Widmer:
Defending a Music Recommender Against Hubness-Based Adversarial Attacks. CoRR abs/2205.12032 (2022) - [i93]Carlos Cancino Chacón, Silvan David Peter, Emmanouil Karystinaios, Francesco Foscarin, Maarten Grachten, Gerhard Widmer:
Partitura: A Python Package for Symbolic Music Processing. CoRR abs/2206.01071 (2022) - [i92]Francesco Foscarin, Emmanouil Karystinaios, Silvan David Peter, Carlos Cancino Chacón, Maarten Grachten, Gerhard Widmer:
The match file format: Encoding Alignments between Scores and Performances. CoRR abs/2206.01104 (2022) - [i91]Paul Primus, Gerhard Widmer:
Improved Zero-Shot Audio Tagging & Classification with Patchout Spectrogram Transformers. CoRR abs/2208.11402 (2022) - [i90]Paul Primus, Gerhard Widmer:
Improving Natural-Language-based Audio Retrieval with Transfer Learning and Audio & Text Augmentations. CoRR abs/2208.11460 (2022) - [i89]Francesco Foscarin, Katharina Hoedt, Verena Praher, Arthur Flexer, Gerhard Widmer:
Concept-Based Techniques for "Musicologist-friendly" Explanations in a Deep Music Classifier. CoRR abs/2208.12485 (2022) - [i88]Emmanouil Karystinaios, Gerhard Widmer:
Cadence Detection in Symbolic Classical Music using Graph Neural Networks. CoRR abs/2208.14819 (2022) - [i87]Florian Schmid, Khaled Koutini, Gerhard Widmer:
Efficient Large-scale Audio Tagging via Transformer-to-CNN Knowledge Distillation. CoRR abs/2211.04772 (2022) - [i86]Khaled Koutini, Shahed Masoudian, Florian Schmid, Hamid Eghbal-zadeh, Jan Schlüter, Gerhard Widmer:
Learning General Audio Representations with Large-Scale Training of Patchout Audio Transformers. CoRR abs/2211.13956 (2022) - [i85]Lukás Samuel Marták, Rainer Kelz, Gerhard Widmer:
Probabilistic Modelling of Signal Mixtures with Differentiable Dictionaries. CoRR abs/2211.15439 (2022) - [i84]Lukás Samuel Marták, Rainer Kelz, Gerhard Widmer:
Differentiable Dictionary Search: Integrating Linear Mixing with Deep Non-Linear Modelling for Audio Source Separation. CoRR abs/2211.15524 (2022) - 2021
- [j47]Florian Henkel, Gerhard Widmer:
Real-Time Music Following in Score Sheet Images via Multi-Resolution Prediction. Frontiers Comput. Sci. 3: 718340 (2021) - [j46]Khaled Koutini, Hamid Eghbal-zadeh, Gerhard Widmer:
Receptive Field Regularization Techniques for Audio Classification and Tagging With Deep Convolutional Neural Networks. IEEE ACM Trans. Audio Speech Lang. Process. 29: 1987-2000 (2021) - [j45]Katharina Prinz, Arthur Flexer, Gerhard Widmer:
On End-to-End White-Box Adversarial Attacks in Music Information Retrieval. Trans. Int. Soc. Music. Inf. Retr. 4(1): 93 (2021) - [c195]Alessandro B. Melchiorre, Verena Haunschmid, Markus Schedl, Gerhard Widmer:
LEMONS: Listenable Explanations for Music recOmmeNder Systems. ECIR (2) 2021: 531-536 - [c194]Florian Henkel, Gerhard Widmer:
Multi-modal Conditional Bounding Box Regression for Music Score Following. EUSIPCO 2021: 356-360 - [c193]Charles Brazier, Gerhard Widmer:
Handling Structural Mismatches in Real-time Opera Tracking. EUSIPCO 2021: 366-370 - [c192]Luís Carvalho, Gerhard Widmer:
Exploiting Temporal Dependencies for Cross-modal Music Piece Identification. EUSIPCO 2021: 386-390 - [c191]Lukás Samuel Marták, Rainer Kelz, Gerhard Widmer:
Probabilistic Modelling of Signal Mixtures with Differentiable Dictionaries. EUSIPCO 2021: 441-445 - [c190]Khaled Koutini, Shahed Masoudian, Florian Schmid, Hamid Eghbal-zadeh, Jan Schlüter, Gerhard Widmer:
Learning General Audio Representations With Large-Scale Training of Patchout Audio Transformers. HEAR@NeurIPS 2021: 65-89 - [c189]Gerhard Widmer:
Con Espressione! AI, Machine Learning, and Musical Expressivity. ICAART (1) 2021: 5 - [c188]Shreyan Chowdhury, Gerhard Widmer:
Towards Explaining Expressive Qualities in Piano Recordings: Transfer of Explanatory Features Via Acoustic Domain Adaptation. ICASSP 2021: 561-565 - [c187]Rainer Kelz, Gerhard Widmer:
Nonlinear Denoising, Linear Demixing. ICBINB@NeurIPS 2021: 54-58 - [c186]Charles Brazier, Gerhard Widmer:
On-Line Audio-to-Lyrics Alignment Based on a Reference Performance. ISMIR 2021: 66-73 - [c185]Shreyan Chowdhury, Gerhard Widmer:
On Perceived Emotion in Expressive Piano Performance: Further Experimental Evidence for the Relevance of Mid-level Perceptual Features. ISMIR 2021: 128-134 - [c184]Verena Praher, Katharina Prinz, Arthur Flexer, Gerhard Widmer:
On the Veracity of Local, Model-agnostic Explanations in Audio Classification: Targeted Investigations with Adversarial Examples. ISMIR 2021: 531-538 - [i83]Shreyan Chowdhury, Gerhard Widmer:
Towards Explaining Expressive Qualities in Piano Recordings: Transfer of Explanatory Features via Acoustic Domain Adaptation. CoRR abs/2102.13479 (2021) - [i82]Florian Henkel, Gerhard Widmer:
Multi-modal Conditional Bounding Box Regression for Music Score Following. CoRR abs/2105.04309 (2021) - [i81]Charles Brazier, Gerhard Widmer:
Handling Structural Mismatches in Real-time Opera Tracking. CoRR abs/2105.08531 (2021) - [i80]Khaled Koutini, Hamid Eghbal-zadeh, Gerhard Widmer:
Receptive Field Regularization Techniques for Audio Classification and Tagging with Deep Convolutional Neural Networks. CoRR abs/2105.12395 (2021) - [i79]Luís Carvalho, Gerhard Widmer:
Exploiting Temporal Dependencies for Cross-Modal Music Piece Identification. CoRR abs/2105.12536 (2021) - [i78]Shreyan Chowdhury, Verena Praher, Gerhard Widmer:
Tracing Back Music Emotion Predictions to Sound Sources and Intuitive Perceptual Qualities. CoRR abs/2106.07787 (2021) - [i77]Khaled Koutini, Hamid Eghbal-zadeh, Florian Henkel, Jan Schlüter, Gerhard Widmer:
Over-Parameterization and Generalization in Audio Classification. CoRR abs/2107.08933 (2021) - [i76]Verena Praher, Katharina Prinz, Arthur Flexer, Gerhard Widmer:
On the Veracity of Local, Model-agnostic Explanations in Audio Classification: Targeted Investigations with Adversarial Examples. CoRR abs/2107.09045 (2021) - [i75]Shreyan Chowdhury, Gerhard Widmer:
On Perceived Emotion in Expressive Piano Performance: Further Experimental Evidence for the Relevance of Mid-level Perceptual Features. CoRR abs/2107.13231 (2021) - [i74]Charles Brazier, Gerhard Widmer:
Improving Real-time Score Following in Opera by Combining Music with Lyrics Tracking. CoRR abs/2110.02592 (2021) - [i73]Khaled Koutini, Jan Schlüter, Hamid Eghbal-zadeh, Gerhard Widmer:
Efficient Training of Audio Transformers with Patchout. CoRR abs/2110.05069 (2021) - [i72]Florian Henkel, Stephanie Schwaiger, Gerhard Widmer:
Fully Automatic Page Turning on Real Scores. CoRR abs/2111.06643 (2021) - 2020
- [c183]Khaled Koutini, Florian Henkel, Hamid Eghbal-Zadeh, Gerhard Widmer:
Low-Complexity Models for Acoustic Scene Classification Based on Receptive Field Regularization and Frequency Damping. DCASE 2020: 86-90 - [c182]Paul Primus, Verena Haunschmid, Patrick Praher, Gerhard Widmer:
Anomalous Sound Detection as a Simple Binary Classification Problem with Careful Selection of Proxy Outlier Examples. DCASE 2020: 170-174 - [c181]Carlos Eduardo Cancino Chacón, Silvan Peter, Shreyan Chowdhury, Anna Aljanaki, Gerhard Widmer:
On the Characterization of Expressive Performance in Classical Music: First Results of the Con Espressione Game. ISMIR 2020: 613-620 - [c180]Florian Henkel, Rainer Kelz, Gerhard Widmer:
Learning to Read and Follow Music in Complete Score Sheet Images. ISMIR 2020: 780-787 - [c179]Hamid Eghbal-zadeh, Florian Henkel, Gerhard Widmer:
Context-Adaptive Reinforcement Learning using Unsupervised Learning of Context Variables. Preregister@NeurIPS 2020: 236-254 - [i71]Charles Brazier, Gerhard Widmer:
Towards Reliable Real-time Opera Tracking: Combining Alignment with Audio Event Detectors to Increase Robustness. CoRR abs/2006.11033 (2020) - [i70]David R. W. Sears, Gerhard Widmer:
Beneath (or beyond) the surface: Discovering voice-leading patterns with skip-grams. CoRR abs/2006.15399 (2020) - [i69]Hamid Eghbal-zadeh, Khaled Koutini, Paul Primus, Verena Haunschmid, Michal Lewandowski, Werner Zellinger, Bernhard Alois Moser, Gerhard Widmer:
On Data Augmentation and Adversarial Risk: An Empirical Analysis. CoRR abs/2007.02650 (2020) - [i68]Florian Henkel, Rainer Kelz, Gerhard Widmer:
Learning to Read and Follow Music in Complete Score Sheet Images. CoRR abs/2007.10736 (2020) - [i67]Khaled Koutini, Hamid Eghbal-Zadeh, Verena Haunschmid, Paul Primus, Shreyan Chowdhury, Gerhard Widmer:
Receptive-Field Regularized CNNs for Music Classification and Tagging. CoRR abs/2007.13503 (2020) - [i66]Verena Haunschmid, Ethan Manilow, Gerhard Widmer:
audioLIME: Listenable Explanations Using Source Separation. CoRR abs/2008.00582 (2020) - [i65]Carlos Eduardo Cancino Chacón, Silvan Peter, Shreyan Chowdhury, Anna Aljanaki, Gerhard Widmer:
On the Characterization of Expressive Performance in Classical Music: First Results of the Con Espressione Game. CoRR abs/2008.02194 (2020) - [i64]Katharina Prinz, Arthur Flexer, Gerhard Widmer:
The Impact of Label Noise on a Music Tagger. CoRR abs/2008.06273 (2020) - [i63]Verena Haunschmid, Ethan Manilow, Gerhard Widmer:
Towards Musically Meaningful Explanations Using Source Separation. CoRR abs/2009.02051 (2020) - [i62]Charles Brazier, Gerhard Widmer:
Addressing the Recitative Problem in Real-time Opera Tracking. CoRR abs/2010.11013 (2020) - [i61]Paul Primus, Verena Haunschmid, Patrick Praher, Gerhard Widmer:
Anomalous Sound Detection as a Simple Binary Classification Problem with Careful Selection of Proxy Outlier Examples. CoRR abs/2011.02949 (2020) - [i60]Khaled Koutini, Florian Henkel, Hamid Eghbal-zadeh, Gerhard Widmer:
Low-Complexity Models for Acoustic Scene Classification Based on Receptive Field Regularization and Frequency Damping. CoRR abs/2011.02955 (2020)
2010 – 2019
- 2019
- [j44]Andreu Vall, Massimo Quadrana, Markus Schedl, Gerhard Widmer:
Order, context and popularity bias in next-song recommendations. Int. J. Multim. Inf. Retr. 8(2): 101-113 (2019) - [j43]Hamid Eghbal-Zadeh, Lukas Fischer, Niko Popitsch, Florian Kromp, Sabine Taschner-Mandl, Teresa Gerber, Eva Bozsaky, Peter F. Ambros, Inge M. Ambros, Gerhard Widmer, Bernhard Alois Moser:
DeepSNP: An End-to-End Deep Neural Network with Attention-Based Localization for Breakpoint Detection in Single-Nucleotide Polymorphism Array Genomic Data. J. Comput. Biol. 26(6): 572-596 (2019) - [j42]Meinard Müller, Andreas Arzt, Stefan Balke, Matthias Dorfer, Gerhard Widmer:
Cross-Modal Music Retrieval and Applications: An Overview of Key Methodologies. IEEE Signal Process. Mag. 36(1): 52-62 (2019) - [j41]Florian Henkel, Stefan Balke, Matthias Dorfer, Gerhard Widmer:
Score Following as a Multi-Modal Reinforcement Learning Problem. Trans. Int. Soc. Music. Inf. Retr. 2(1): 67-81 (2019) - [j40]Andreu Vall, Matthias Dorfer, Hamid Eghbal-zadeh, Markus Schedl, Keki Burjorjee, Gerhard Widmer:
Feature-combination hybrid recommender systems for automated music playlist continuation. User Model. User Adapt. Interact. 29(2): 527-572 (2019) - [c178]Hamid Eghbal-zadeh, Werner Zellinger, Gerhard Widmer:
Mixture Density Generative Adversarial Networks. CVPR 2019: 5820-5829 - [c177]Khaled Koutini, Hamid Eghbal-zadeh, Gerhard Widmer:
Receptive-Field-Regularized CNN Variants for Acoustic Scene Classification. DCASE 2019: 124-128 - [c176]Paul Primus, Hamid Eghbal-zadeh, David Eitelsebner, Khaled Koutini, Andreas Arzt, Gerhard Widmer:
Exploiting Parallel Audio Recordings to Enforce Device Invariance in CNN-based Acoustic Scene Classification. DCASE 2019: 204-208 - [c175]Khaled Koutini, Hamid Eghbal-zadeh, Matthias Dorfer, Gerhard Widmer:
The Receptive Field as a Regularizer in Deep Convolutional Neural Networks for Acoustic Scene Classification. EUSIPCO 2019: 1-5 - [c174]Rainer Kelz, Sebastian Böck, Gerhard Widmer:
Deep Polyphonic ADSR Piano Note Transcription. ICASSP 2019: 246-250 - [c173]Stefan Balke, Matthias Dorfer, Luís Carvalho, Andreas Arzt, Gerhard Widmer:
Learning Soft-Attention Models for Tempo-invariant Audio-Sheet Music Retrieval. ISMIR 2019: 216-222 - [c172]Shreyan Chowdhury, Andreu Vall, Verena Haunschmid, Gerhard Widmer:
Towards Explainable Music Emotion Recognition: The Route via Mid-level Features. ISMIR 2019: 237-243 - [c171]Rainer Kelz, Gerhard Widmer:
Towards Interpretable Polyphonic Transcription with Invertible Neural Networks. ISMIR 2019: 376-383 - [c170]Thassilo Gadermaier, Gerhard Widmer:
A Study of Annotation and Alignment Accuracy for Performance Comparison in Complex Orchestral Music. ISMIR 2019: 769-775 - [c169]Federico Simonetta, Carlos Eduardo Cancino Chacón, Stavros Ntalampiras, Gerhard Widmer:
A Convolutional Approach to Melody Line Identification in Symbolic Scores. ISMIR 2019: 924-931 - [c168]Khaled Koutini, Shreyan Chowdhury, Verena Haunschmid, Hamid Eghbal-Zadeh, Gerhard Widmer:
Emotion and Theme Recognition in Music with Frequency-Aware RF-Regularized CNNs. MediaEval 2019 - [d1]Thassilo Gadermaier, Gerhard Widmer:
A Study of Annotation and Alignment Accuracy for Performance Comparison in Complex Orchestral Music. Zenodo, 2019
- [i59]Rainer Kelz, Sebastian Böck, Gerhard Widmer:
Multitask Learning for Polyphonic Piano Transcription, a Case Study. CoRR abs/1902.04390 (2019) - [i58]Meinard Müller, Andreas Arzt, Stefan Balke, Matthias Dorfer, Gerhard Widmer:
Cross-Modal Music Retrieval and Applications: An Overview of Key Methodologies. CoRR abs/1902.04397 (2019) - [i57]Verena Haunschmid, Shreyan Chowdhury, Gerhard Widmer:
Two-level Explanations in Music Emotion Recognition. CoRR abs/1905.11760 (2019) - [i56]Zhengshan Shi, Carlos Cancino Chacón, Gerhard Widmer:
User Curated Shaping of Expressive Performances. CoRR abs/1906.06428 (2019) - [i55]Rainer Kelz, Sebastian Böck, Gerhard Widmer:
Deep Polyphonic ADSR Piano Note Transcription. CoRR abs/1906.09165 (2019) - [i54]Federico Simonetta, Carlos Eduardo Cancino Chacón, Stavros Ntalampiras, Gerhard Widmer:
A Convolutional Approach to Melody Line Identification in Symbolic Scores. CoRR abs/1906.10547 (2019) - [i53]Stefan Balke, Matthias Dorfer, Luís Carvalho, Andreas Arzt, Gerhard Widmer:
Learning Soft-Attention Models for Tempo-invariant Audio-Sheet Music Retrieval. CoRR abs/1906.10996 (2019) - [i52]Khaled Koutini, Hamid Eghbal-zadeh, Matthias Dorfer, Gerhard Widmer:
The Receptive Field as a Regularizer in Deep Convolutional Neural Networks for Acoustic Scene Classification. CoRR abs/1907.01803 (2019) - [i51]Shreyan Chowdhury, Andreu Vall, Verena Haunschmid, Gerhard Widmer:
Towards Explainable Music Emotion Recognition: The Route via Mid-level Features. CoRR abs/1907.03572 (2019) - [i50]Rainer Kelz, Gerhard Widmer:
Towards Interpretable Polyphonic Transcription with Invertible Neural Networks. CoRR abs/1909.01622 (2019) - [i49]Khaled Koutini, Hamid Eghbal-zadeh, Gerhard Widmer:
Receptive-field-regularized CNN variants for acoustic scene classification. CoRR abs/1909.02859 (2019) - [i48]Paul Primus, Hamid Eghbal-zadeh, David Eitelsebner, Khaled Koutini, Andreas Arzt, Gerhard Widmer:
Exploiting Parallel Audio Recordings to Enforce Device Invariance in CNN-based Acoustic Scene Classification. CoRR abs/1909.02869 (2019) - [i47]Florian Henkel, Rainer Kelz, Gerhard Widmer:
Audio-Conditioned U-Net for Position Estimation in Full Sheet Images. CoRR abs/1910.07254 (2019) - [i46]Thassilo Gadermaier, Gerhard Widmer:
A Study of Annotation and Alignment Accuracy for Performance Comparison in Complex Orchestral Music. CoRR abs/1910.07394 (2019) - [i45]Khaled Koutini, Shreyan Chowdhury, Verena Haunschmid, Hamid Eghbal-zadeh, Gerhard Widmer:
Emotion and Theme Recognition in Music with Frequency-Aware RF-Regularized CNNs. CoRR abs/1911.05833 (2019) - 2018
- [j39]Carlos Eduardo Cancino Chacón, Maarten Grachten, Werner Goebl, Gerhard Widmer:
Computational Models of Expressive Music Performance: A Comprehensive and Critical Review. Frontiers Digit. Humanit. 5: 25 (2018) - [j38]Matthias Dorfer, Jan Schlüter, Andreu Vall, Filip Korzeniowski, Gerhard Widmer:
End-to-end cross-modality retrieval with CCA projections and pairwise ranking loss. Int. J. Multim. Inf. Retr. 7(2): 117-128 (2018) - [j37]Bernhard Lehner, Jan Schlüter, Gerhard Widmer:
Online, Loudness-Invariant Vocal Detection in Mixed Music Signals. IEEE ACM Trans. Audio Speech Lang. Process. 26(8): 1369-1380 (2018) - [j36]Chih-Wei Wu, Christian Dittmar, Carl Southall, Richard Vogl, Gerhard Widmer, Jason Hockman, Meinard Müller, Alexander Lerch:
A Review of Automatic Drum Transcription. IEEE ACM Trans. Audio Speech Lang. Process. 26(9): 1457-1483 (2018) - [j35]Matthias Dorfer, Jan Hajic Jr., Andreas Arzt, Harald Frostel, Gerhard Widmer:
Learning Audio-Sheet Music Correspondences for Cross-Modal Retrieval and Piece Identification. Trans. Int. Soc. Music. Inf. Retr. 1(1): 22-31 (2018) - [c167]Khaled Koutini, Hamid Eghbal-zadeh, Gerhard Widmer:
Iterative knowledge distillation in R-CNNs for weakly-labeled semi-supervised sound event detection. DCASE 2018: 173-177 - [c166]Matthias Dorfer, Gerhard Widmer:
Training general-purpose audio tagging networks with noisy labels and iterative self-verification. DCASE 2018: 178-182 - [c165]Filip Korzeniowski, Gerhard Widmer:
Automatic Chord Recognition with Higher-Order Harmonic Language Modelling. EUSIPCO 2018: 1900-1904 - [c164]Filip Korzeniowski, David R. W. Sears, Gerhard Widmer:
A Large-Scale Study of Language Models for Chord Prediction. ICASSP 2018: 91-95 - [c163]Rainer Kelz, Gerhard Widmer:
Investigating Label Noise Sensitivity of Convolutional Neural Networks for Fine Grained Audio Signal Labelling. ICASSP 2018: 2996-3000 - [c162]Filip Korzeniowski, Gerhard Widmer:
Improved Chord Recognition by Combining Duration and Harmonic Language Models. ISMIR 2018: 10-17 - [c161]Stefan Lattner, Maarten Grachten, Gerhard Widmer:
A Predictive Model for Music based on Learned Interval Representations. ISMIR 2018: 26-33 - [c160]David R. W. Sears, Filip Korzeniowski, Gerhard Widmer:
Evaluating Language Models of Tonal Harmony. ISMIR 2018: 211-217 - [c159]Jan Hajic Jr., Matthias Dorfer, Gerhard Widmer, Pavel Pecina:
Towards Full-Pipeline Handwritten OMR with Musical Symbol Detection by U-Nets. ISMIR 2018: 225-232 - [c158]Filip Korzeniowski, Gerhard Widmer:
Genre-Agnostic Key Classification With Convolutional Neural Networks. ISMIR 2018: 264-270 - [c157]Stefan Lattner, Maarten Grachten, Gerhard Widmer:
Learning Interval Representations from Polyphonic Music Sequences. ISMIR 2018: 661-668 - [c156]Matthias Dorfer, Florian Henkel, Gerhard Widmer:
Learning to Listen, Read, and Follow: Score Following as a Reinforcement Learning Game. ISMIR 2018: 784-791 - [c155]Andreu Vall, Gerhard Widmer:
Machine Learning Approaches to Hybrid Music Recommender Systems. ECML/PKDD (3) 2018: 639-642 - [c154]Andreu Vall, Matthias Dorfer, Markus Schedl, Gerhard Widmer:
A hybrid approach to music playlist continuation based on playlist-song membership. SAC 2018: 1374-1382 - [i44]Filip Korzeniowski, David R. W. Sears, Gerhard Widmer:
A Large-Scale Study of Language Models for Chord Prediction. CoRR abs/1804.01849 (2018) - [i43]Andreu Vall, Matthias Dorfer, Markus Schedl, Gerhard Widmer:
A Hybrid Approach to Music Playlist Continuation Based on Playlist-Song Membership. CoRR abs/1805.09557 (2018) - [i42]Rainer Kelz, Gerhard Widmer:
Investigating Label Noise Sensitivity of Convolutional Neural Networks for Fine Grained Audio Signal Labelling. CoRR abs/1805.10880 (2018) - [i41]Rainer Kelz, Gerhard Widmer:
Learning to Transcribe by Ear. CoRR abs/1805.11526 (2018) - [i40]Richard Vogl, Gerhard Widmer, Peter Knees:
Towards multi-instrument drum transcription. CoRR abs/1806.06676 (2018) - [i39]Stefan Lattner, Maarten Grachten, Gerhard Widmer:
Learning Transposition-Invariant Interval Features from Symbolic Music and Audio. CoRR abs/1806.08236 (2018) - [i38]Stefan Lattner, Maarten Grachten, Gerhard Widmer:
A Predictive Model for Music Based on Learned Interval Representations. CoRR abs/1806.08686 (2018) - [i37]David R. W. Sears, Filip Korzeniowski, Gerhard Widmer:
Evaluating language models of tonal harmony. CoRR abs/1806.08724 (2018) - [i36]Hamid Eghbal-zadeh, Lukas Fischer, Niko Popitsch, Florian Kromp, Sabine Taschner-Mandl, Khaled Koutini, Teresa Gerber, Eva Bozsaky, Peter F. Ambros, Inge M. Ambros, Gerhard Widmer, Bernhard Alois Moser:
Deep SNP: An End-to-end Deep Neural Network with Attention-based Localization for Break-point Detection in SNP Array Genomic data. CoRR abs/1806.08840 (2018) - [i35]Anna Aljanaki, Gerhard Widmer:
Modeling Majorness as a Perceptual Property in Music from Listener Ratings. CoRR abs/1806.10570 (2018) - [i34]Andreu Vall, Massimo Quadrana, Markus Schedl, Gerhard Widmer:
The Importance of Song Context and Song Order in Automated Music Playlist Generation. CoRR abs/1807.04690 (2018) - [i33]Andreu Vall, Gerhard Widmer:
Machine Learning Approaches to Hybrid Music Recommender Systems. CoRR abs/1807.05858 (2018) - [i32]Matthias Dorfer, Florian Henkel, Gerhard Widmer:
Learning to Listen, Read, and Follow: Score Following as a Reinforcement Learning Game. CoRR abs/1807.06391 (2018) - [i31]David R. W. Sears, Gerhard Widmer:
Psychological constraints on string-based methods for pattern discovery in polyphonic corpora. CoRR abs/1807.06700 (2018) - [i30]Filip Korzeniowski, Gerhard Widmer:
Improved Chord Recognition by Combining Duration and Harmonic Language Models. CoRR abs/1808.05335 (2018) - [i29]Filip Korzeniowski, Gerhard Widmer:
Genre-Agnostic Key Classification With Convolutional Neural Networks. CoRR abs/1808.05340 (2018) - [i28]Filip Korzeniowski, Gerhard Widmer:
Automatic Chord Recognition with Higher-Order Harmonic Language Modelling. CoRR abs/1808.05341 (2018) - [i27]Matthias Dorfer, Jan Hajic Jr., Gerhard Widmer:
Attention as a Perspective for Learning Tempo-invariant Audio Queries. CoRR abs/1809.05689 (2018) - [i26]Hamid Eghbal-zadeh, Werner Zellinger, Gerhard Widmer:
Mixture Density Generative Adversarial Networks. CoRR abs/1811.00152 (2018) - 2017
- [j34]Maarten Grachten, Carlos Eduardo Cancino Chacón, Thassilo Gadermaier, Gerhard Widmer:
Toward Computer-Assisted Understanding of Dynamics in Symphonic Music. IEEE Multim. 24(1): 36-46 (2017) - [j33]Carlos Eduardo Cancino Chacón, Thassilo Gadermaier, Gerhard Widmer, Maarten Grachten:
An evaluation of linear and non-linear models of expressive dynamics in classical piano and symphonic music. Mach. Learn. 106(6): 887-909 (2017) - [j32]Gerhard Widmer:
Getting Closer to the Essence of Music: The Con Espressione Manifesto. ACM Trans. Intell. Syst. Technol. 8(2): 19:1-19:13 (2017) - [c153]Ali Nikrang, David R. W. Sears, Gerhard Widmer:
Automatic Estimation of Harmonic Tension by Distributed Representation of Chords. CMMR 2017: 23-34 - [c152]Filip Korzeniowski, Gerhard Widmer:
End-to-end musical key estimation using a convolutional neural network. EUSIPCO 2017: 966-970 - [c151]Hamid Eghbal-zadeh, Bernhard Lehner, Matthias Dorfer, Gerhard Widmer:
A hybrid approach with multi-channel i-vectors and convolutional neural networks for acoustic scene classification. EUSIPCO 2017: 2749-2753 - [c150]Matthias Dorfer, Jan Hajic, Gerhard Widmer:
On the Potential of Fully Convolutional Neural Networks for Musical Symbol Detection. GREC@ICDAR 2017: 53-54 - [c149]Matthias Dorfer, Andreas Arzt, Gerhard Widmer:
Learning Audio-Sheet Music Correspondences for Score Identification and Offline Alignment. ISMIR 2017: 115-122 - [c148]Richard Vogl, Matthias Dorfer, Gerhard Widmer, Peter Knees:
Drum Transcription via Joint Beat and Drum Modeling Using Convolutional Recurrent Neural Networks. ISMIR 2017: 150-157 - [c147]David R. W. Sears, Andreas Arzt, Harald Frostel, Reinhard Sonnleitner, Gerhard Widmer:
Modeling Harmony with Skip-Grams. ISMIR 2017: 332-338 - [c146]Andreas Arzt, Gerhard Widmer:
Piece Identification in Classical Piano Music Without Reference Scores. ISMIR 2017: 354-360 - [c145]Andreu Vall, Hamid Eghbal-zadeh, Matthias Dorfer, Markus Schedl, Gerhard Widmer:
Music Playlist Continuation by Learning from Hand-Curated Examples and Song Features: Alleviating the Cold-Start Problem for Rare and Out-of-Set Songs. DLRS@RecSys 2017: 46-54 - [c144]Andreu Vall, Massimo Quadrana, Markus Schedl, Gerhard Widmer, Paolo Cremonesi:
The Importance of Song Context in Music Playlists. RecSys Posters 2017 - [c143]Rainer Kelz, Gerhard Widmer:
An Experimental Analysis of the Entanglement Problem in Neural-Network-based Music Transcription Systems. Semantic Audio 2017 - [c142]Filip Korzeniowski, Gerhard Widmer:
On the Futility of Learning Complex Frame-Level Language Models for Chord Recognition. Semantic Audio 2017 - [i25]Rainer Kelz, Gerhard Widmer:
An Experimental Analysis of the Entanglement Problem in Neural-Network-based Music Transcription Systems. CoRR abs/1702.00025 (2017) - [i24]Filip Korzeniowski, Gerhard Widmer:
On the Futility of Learning Complex Frame-Level Language Models for Chord Recognition. CoRR abs/1702.00178 (2017) - [i23]Matthias Dorfer, Jan Schlüter, Andreu Vall, Filip Korzeniowski, Gerhard Widmer:
End-to-End Cross-Modality Retrieval with CCA Projections and Pairwise Ranking Loss. CoRR abs/1705.06979 (2017) - [i22]Filip Korzeniowski, Gerhard Widmer:
End-to-End Musical Key Estimation Using a Convolutional Neural Network. CoRR abs/1706.02921 (2017) - [i21]Hamid Eghbal-zadeh, Bernhard Lehner, Matthias Dorfer, Gerhard Widmer:
A Hybrid Approach with Multi-channel I-Vectors and Convolutional Neural Networks for Acoustic Scene Classification. CoRR abs/1706.06525 (2017) - [i20]Ali Nikrang, David R. W. Sears, Gerhard Widmer:
Automatic estimation of harmonic tension by distributed representation of chords. CoRR abs/1707.00972 (2017) - [i19]David R. W. Sears, Andreas Arzt, Harald Frostel, Reinhard Sonnleitner, Gerhard Widmer:
Modeling Harmony with Skip-Grams. CoRR abs/1707.04457 (2017) - [i18]Hamid Eghbal-zadeh, Gerhard Widmer:
Likelihood Estimation for Generative Adversarial Networks. CoRR abs/1707.07530 (2017) - [i17]Matthias Dorfer, Andreas Arzt, Gerhard Widmer:
Learning Audio - Sheet Music Correspondences for Score Identification and Offline Alignment. CoRR abs/1707.09887 (2017) - [i16]Andreas Arzt, Gerhard Widmer:
Piece Identification in Classical Piano Music Without Reference Scores. CoRR abs/1708.00733 (2017) - [i15]Hamid Eghbal-zadeh, Gerhard Widmer:
Probabilistic Generative Adversarial Networks. CoRR abs/1708.01886 (2017) - [i14]Stefan Lattner, Maarten Grachten, Gerhard Widmer:
Learning Musical Relations using Gated Autoencoders. CoRR abs/1708.05325 (2017) - [i13]Carlos Eduardo Cancino Chacón, Maarten Grachten, David R. W. Sears, Gerhard Widmer:
What were you expecting? Using Expectancy Features to Predict Expressive Performances of Classical Piano Music. CoRR abs/1709.03629 (2017) - [i12]Carlos Cancino Chacón, Martin Bonev, Amaury Durand, Maarten Grachten, Andreas Arzt, Laura Bishop, Werner Goebl, Gerhard Widmer:
The ACCompanion v0.1: An Expressive Accompaniment System. CoRR abs/1711.02427 (2017) - [i11]Hamid Eghbal-zadeh, Matthias Dorfer, Gerhard Widmer:
Deep Within-Class Covariance Analysis for Acoustic Scene Classification. CoRR abs/1711.04022 (2017) - 2016
- [j31]Reinhard Sonnleitner, Gerhard Widmer:
Robust Quad-Based Audio Fingerprinting. IEEE ACM Trans. Audio Speech Lang. Process. 24(3): 409-421 (2016) - [c141]Filip Korzeniowski, Gerhard Widmer:
Feature Learning for Chord Recognition: The Deep Chroma Extractor. ISMIR 2016: 37-43 - [c140]Florian Krebs, Sebastian Böck, Matthias Dorfer, Gerhard Widmer:
Downbeat Tracking Using Beat Synchronous Features with Recurrent Neural Networks. ISMIR 2016: 129-135 - [c139]Reinhard Sonnleitner, Andreas Arzt, Gerhard Widmer:
Landmark-Based Audio Fingerprinting for DJ Mix Monitoring. ISMIR 2016: 185-191 - [c138]Sebastian Böck, Florian Krebs, Gerhard Widmer:
Joint Beat and Downbeat Tracking with Recurrent Neural Networks. ISMIR 2016: 255-261 - [c137]Rainer Kelz, Matthias Dorfer, Filip Korzeniowski, Sebastian Böck, Andreas Arzt, Gerhard Widmer:
On the Potential of Simple Framewise Approaches to Piano Transcription. ISMIR 2016: 475-481 - [c136]Hamid Eghbal-zadeh, Gerhard Widmer:
Noise Robust Music Artist Recognition Using I-Vector Features. ISMIR 2016: 709-715 - [c135]Matthias Dorfer, Andreas Arzt, Gerhard Widmer:
Towards Score Following In Sheet Music Images. ISMIR 2016: 789-795 - [c134]Filip Korzeniowski, Gerhard Widmer:
A fully convolutional deep auditory model for musical chord recognition. MLSP 2016: 1-6 - [c133]Sebastian Böck, Filip Korzeniowski, Jan Schlüter, Florian Krebs, Gerhard Widmer:
madmom: A New Python Audio and Music Signal Processing Library. ACM Multimedia 2016: 1174-1178 - [c132]Matthias Dorfer, Rainer Kelz, Gerhard Widmer:
Deep Linear Discriminant Analysis. ICLR (Poster) 2016 - [p3]Tom Collins, Andreas Arzt, Harald Frostel, Gerhard Widmer:
Using Geometric Symbolic Fingerprinting to Discover Distinctive Patterns in Polyphonic Music Corpora. Computational Music Analysis 2016: 445-474 - [i10]Sebastian Böck, Filip Korzeniowski, Jan Schlüter, Florian Krebs, Gerhard Widmer:
madmom: a new Python Audio and Music Signal Processing Library. CoRR abs/1605.07008 (2016) - [i9]Gerhard Widmer:
Getting Closer to the Essence of Music: The Con Espressione Manifesto. CoRR abs/1611.09733 (2016) - [i8]Maarten Grachten, Carlos Eduardo Cancino Chacón, Thassilo Gadermaier, Gerhard Widmer:
Towards computer-assisted understanding of dynamics in symphonic music. CoRR abs/1612.02198 (2016) - [i7]Stefan Lattner, Maarten Grachten, Gerhard Widmer:
Imposing higher-level Structure in Polyphonic Music Generation using Convolutional Restricted Boltzmann Machines and Constraints. CoRR abs/1612.04742 (2016) - [i6]Matthias Dorfer, Andreas Arzt, Gerhard Widmer:
Towards Score Following in Sheet Music Images. CoRR abs/1612.05050 (2016) - [i5]Filip Korzeniowski, Gerhard Widmer:
Feature Learning for Chord Recognition: The Deep Chroma Extractor. CoRR abs/1612.05065 (2016) - [i4]Matthias Dorfer, Andreas Arzt, Gerhard Widmer:
Towards End-to-End Audio-Sheet-Music Retrieval. CoRR abs/1612.05070 (2016) - [i3]Matthias Dorfer, Andreas Arzt, Sebastian Böck, Amaury Durand, Gerhard Widmer:
Live Score Following on Sheet Music Images. CoRR abs/1612.05076 (2016) - [i2]Filip Korzeniowski, Gerhard Widmer:
A Fully Convolutional Deep Auditory Model for Musical Chord Recognition. CoRR abs/1612.05082 (2016) - [i1]Rainer Kelz, Matthias Dorfer, Filip Korzeniowski, Sebastian Böck, Andreas Arzt, Gerhard Widmer:
On the Potential of Simple Framewise Approaches to Piano Transcription. CoRR abs/1612.05153 (2016) - 2015
- [j30]Florian Krebs, Andre Holzapfel, Ali Taylan Cemgil, Gerhard Widmer:
Inferring Metrical Structure in Music Using Particle Filters. IEEE ACM Trans. Audio Speech Lang. Process. 23(5): 817-827 (2015) - [c131]Bernhard Lehner, Gerhard Widmer, Sebastian Böck:
A low-latency, real-time-capable singing voice detection method with LSTM recurrent neural networks. EUSIPCO 2015: 21-25 - [c130]Hamid Eghbal-zadeh, Markus Schedl, Gerhard Widmer:
Timbral modeling for music artist recognition using i-vectors. EUSIPCO 2015: 1286-1290 - [c129]Andreas Arzt, Harald Frostel, Thassilo Gadermaier, Martin Gasser, Maarten Grachten, Gerhard Widmer:
Artificial Intelligence in the Concertgebouw. IJCAI 2015: 2424-2430 - [c128]Bernhard Lehner, Gerhard Widmer, Reinhard Sonnleitner:
Improving voice activity detection in movies. INTERSPEECH 2015: 2942-2946 - [c127]Florian Krebs, Sebastian Böck, Gerhard Widmer:
An Efficient State-Space Model for Joint Tempo and Meter Tracking. ISMIR 2015: 72-78 - [c126]Bernhard Lehner, Gerhard Widmer:
Monaural Blind Source Separation in the Context of Vocal Detection. ISMIR 2015: 309-315 - [c125]Andreas Arzt, Gerhard Widmer:
Real-Time Music Tracking Using Multiple Performances as a Reference. ISMIR 2015: 357-363 - [c124]Hamid Eghbal-zadeh, Bernhard Lehner, Markus Schedl, Gerhard Widmer:
I-Vectors for Timbre-Based Music Similarity and Music Artist Classification. ISMIR 2015: 554-560 - [c123]Martin Gasser, Andreas Arzt, Thassilo Gadermaier, Maarten Grachten, Gerhard Widmer:
Classical Music on the Web - User Interfaces and Data Representations. ISMIR 2015: 571-577 - [c122]Christian Dittmar, Bernhard Lehner, Thomas Prätzlich, Meinard Müller, Gerhard Widmer:
Cross-Version Singing Voice Detection in Classical Opera Recordings. ISMIR 2015: 618-624 - [c121]Sebastian Böck, Florian Krebs, Gerhard Widmer:
Accurate Tempo Estimation Based on Recurrent Neural Networks and Resonating Comb Filters. ISMIR 2015: 625-631 - 2014
- [c120]Reinhard Sonnleitner, Gerhard Widmer:
Quad-Based Audio Fingerprinting Robust to Time and Frequency Scaling. DAFx 2014: 173-180 - [c119]Andreas Arzt, Sebastian Böck, Sebastian Flossmann, Harald Frostel, Martin Gasser, Cynthia C. S. Liem, Gerhard Widmer:
The Piano Music Companion. ECAI 2014: 1221-1222 - [c118]Florian Krebs, Filip Korzeniowski, Maarten Grachten, Gerhard Widmer:
Unsupervised learning and refinement of rhythmic patterns for beat and downbeat tracking. EUSIPCO 2014: 611-615 - [c117]Bernhard Lehner, Gerhard Widmer, Reinhard Sonnleitner:
On the reduction of false positives in singing voice detection. ICASSP 2014: 7480-7484 - [c116]Filip Korzeniowski, Sebastian Böck, Gerhard Widmer:
Probabilistic Extraction of Beat Positions from a Beat Activation Function. ISMIR 2014: 513-518 - [c115]Andreas Arzt, Gerhard Widmer, Reinhard Sonnleitner:
Tempo- and Transposition-invariant Identification of Piece and Score Position. ISMIR 2014: 549-554 - [c114]Sebastian Böck, Florian Krebs, Gerhard Widmer:
A Multi-model Approach to Beat Tracking Considering Heterogeneous Music Styles. ISMIR 2014: 603-608 - [c113]Tom Collins, Sebastian Böck, Florian Krebs, Gerhard Widmer:
Bridging the Audio-Symbolic Gap: The Discovery of Repeated Note Content Directly from Polyphonic Music Audio. Semantic Audio 2014 - 2013
- [c112]Filip Korzeniowski, Florian Krebs, Andreas Arzt, Gerhard Widmer:
Tracking rests and Tempo changes: Improved Score following with Particle filters. ICMC 2013 - [c111]Bernhard Lehner, Reinhard Sonnleitner, Gerhard Widmer:
Towards Light-Weight, Real-Time-Capable Singing Voice Detection. ISMIR 2013: 53-58 - [c110]Florian Krebs, Sebastian Böck, Gerhard Widmer:
Rhythmic Pattern Modeling for Beat and Downbeat Tracking in Musical Audio. ISMIR 2013: 227-232 - [c109]Sebastian Böck, Gerhard Widmer:
Local Group Delay Based Vibrato and Tremolo Suppression for Onset Detection. ISMIR 2013: 361-366 - [c108]Tom Collins, Andreas Arzt, Sebastian Flossmann, Gerhard Widmer:
SIARCT-CFP: Improving Precision and the Discovery of Inexact Musical Patterns in Point-Set Representations. ISMIR 2013: 549-554 - [c107]Maarten Grachten, Martin Gasser, Andreas Arzt, Gerhard Widmer:
Automatic Alignment of Music Performances with Structural Differences. ISMIR 2013: 607-612 - [p2]Sebastian Flossmann, Maarten Grachten, Gerhard Widmer:
Expressive Performance Rendering with Probabilistic Models. Guide to Computing for Expressive Music Performance 2013: 75-98 - 2012
- [j29]Dominik Schnitzer, Arthur Flexer, Markus Schedl, Gerhard Widmer:
Local and global scaling reduce hubs in space. J. Mach. Learn. Res. 13: 2871-2902 (2012) - [j28]Peter Knees, Tim Pohle, Gerhard Widmer:
Sound/tracks: artistic real-time sonification of train journeys. J. Multimodal User Interfaces 6(1-2): 87-93 (2012) - [j27]Dominik Schnitzer, Arthur Flexer, Gerhard Widmer:
A fast audio similarity retrieval method for millions of music tracks. Multim. Tools Appl. 58(1): 23-40 (2012) - [c106]Andreas Arzt, Gerhard Widmer, Sebastian Böck, Reinhard Sonnleitner, Harald Frostel:
Towards a Complete Classical Music Companion. ECAI 2012: 67-72 - [c105]Andreas Arzt, Gerhard Widmer, Simon Dixon:
Adaptive distance normalization for real-time music tracking. EUSIPCO 2012: 2689-2693 - [c104]Andreas Arzt, Sebastian Böck, Gerhard Widmer:
Fast Identification of Piece and Score Position via Symbolic Fingerprinting. ISMIR 2012: 433-438 - 2011
- [j26]Markus Schedl, Gerhard Widmer, Peter Knees, Tim Pohle:
A music information system automatically generated via Web content mining techniques. Inf. Process. Manag. 47(3): 426-439 (2011) - [j25]Markus Schedl, Tim Pohle, Peter Knees, Gerhard Widmer:
Exploring the music similarity space on the web. ACM Trans. Inf. Syst. 29(3): 14:1-14:24 (2011) - [c103]Dominik Schnitzer, Arthur Flexer, Markus Schedl, Gerhard Widmer:
Using Mutual Proximity to Improve Content-Based Audio Similarity. ISMIR 2011: 79-84 - [c102]Bernhard Niedermayer, Sebastian Böck, Gerhard Widmer:
On the Importance of "Real" Audio Data for MIR Algorithm Evaluation at the Note-Level - A Comparative Study. ISMIR 2011: 543-548 - 2010
- [j24]Werner Goebl, Sebastian Flossmann, Gerhard Widmer:
Investigations of Between-Hand Synchronization in Magaloff's Chopin. Comput. Music. J. 34(3): 35-44 (2010) - [c101]Klaus Seyerlehner, Gerhard Widmer, Peter Knees:
A Comparison of Human, Automatic and Collaborative Music Genre Classification and User Centric Evaluation of Genre Classification Systems. Adaptive Multimedia Retrieval 2010: 118-131 - [c100]Miguel Molina-Solana, Maarten Grachten, Gerhard Widmer:
Evidence for Pianist-specific Rubato Style in Chopin Nocturnes. ISMIR 2010: 225-230 - [c99]Dominik Schnitzer, Arthur Flexer, Gerhard Widmer, Martin Gasser:
Islands of Gaussians: The Self Organizing Map and Gaussian Music Similarity Features. ISMIR 2010: 327-332 - [c98]Bernhard Niedermayer, Gerhard Widmer:
A Multi-pass Algorithm for Accurate Audio-to-Score Alignment. ISMIR 2010: 417-422 - [c97]Peter Knees, Markus Schedl, Tim Pohle, Klaus Seyerlehner, Gerhard Widmer:
Supervised and Unsupervised Web Document Filtering Techniques to Improve Text-Based Music Retrieval. ISMIR 2010: 543-548 - [c96]Markus Schedl, Klaus Seyerlehner, Dominik Schnitzer, Gerhard Widmer, Cornelia Schiketanz:
Three web-based heuristics to determine a person's or institution's country of origin. SIGIR 2010: 801-802 - [c95]Andreas Arzt, Gerhard Widmer:
Towards Effective 'Any-Time' Music Tracking. STAIRS 2010: 24-36
2000 – 2009
- 2009
- [j23]Gerhard Widmer, Sebastian Flossmann, Maarten Grachten:
YQX Plays Chopin. AI Mag. 30(3): 35-48 (2009) - [c94]Maarten Grachten, Gerhard Widmer:
Who Is Who in the End? Recognizing Pianists by Their Final Ritardandi. ISMIR 2009: 51-56 - [c93]Maarten Grachten, Markus Schedl, Tim Pohle, Gerhard Widmer:
The ISMIR Cloud: A Decade of ISMIR Conferences at Your Fingertips. ISMIR 2009: 63-68 - [c92]Klaus Seyerlehner, Peter Knees, Dominik Schnitzer, Gerhard Widmer:
Browsing Music Recommendation Networks. ISMIR 2009: 129-134 - [c91]Tim Pohle, Dominik Schnitzer, Markus Schedl, Peter Knees, Gerhard Widmer:
On Rhythm and General Music Similarity. ISMIR 2009: 525-530 - [c90]Dominik Schnitzer, Arthur Flexer, Gerhard Widmer:
A Filter-and-Refine Indexing Method for Fast Similarity Search in Millions of Music Tracks. ISMIR 2009: 537-542 - [c89]Peter Knees, Tim Pohle, Markus Schedl, Dominik Schnitzer, Klaus Seyerlehner, Gerhard Widmer:
Augmenting Text-based Music Retrieval with Audio Similarity: Advantages and Limitations. ISMIR 2009: 579-584 - [c88]Gerhard Widmer:
Dealing with Music in Intelligent Ways. ISMIS 2009: 2 - [c87]Klaus Seyerlehner, Arthur Flexer, Gerhard Widmer:
On the limitations of browsing top-N recommender systems. RecSys 2009: 321-324 - 2008
- [j22]Craig Saunders, David R. Hardoon, John Shawe-Taylor, Gerhard Widmer:
Using string kernels to identify famous performers from their playing style. Intell. Data Anal. 12(4): 425-440 (2008) - [c86]Tim Pohle, Klaus Seyerlehner, Gerhard Widmer:
An Approach to Automatically Tracking Music Preference on Mobile Players. Adaptive Multimedia Retrieval 2008: 66-77 - [c85]Søren Tjagvad Madsen, Rainer Typke, Gerhard Widmer:
Automatic Reduction of MIDI Files Preserving Relevant Musical Content. Adaptive Multimedia Retrieval 2008: 89-99 - [c84]Andreas Arzt, Gerhard Widmer, Simon Dixon:
Automatic Page Turning for Musicians via Real-Time Machine Listening. ECAI 2008: 241-245 - [c83]Markus Schedl, Peter Knees, Tim Pohle, Gerhard Widmer:
Towards an Automatically Generated Music Information System Via Web Content Mining. ECIR 2008: 585-590 - [c82]Christoph Anthes, Vassil Alexandrov, Dieter Kranzlmüller, Jens Volkert, Gerhard Widmer:
Collaborative and Cooperative Environments. ICCS (3) 2008: 379-380 - [c81]Arthur Flexer, Dominik Schnitzer, Martin Gasser, Gerhard Widmer:
Playlist Generation using Start and End Songs. ISMIR 2008: 173-178 - [c80]Martin Gasser, Arthur Flexer, Gerhard Widmer:
Streamcatcher: Integrated Visualization of Music Clips and Online Audio Streams. ISMIR 2008: 205-210 - [c79]Tim Pohle, Peter Knees, Gerhard Widmer:
Sound/tracks: real-time synaesthetic sonification and visualisation of passing landscapes. ACM Multimedia 2008: 599-608 - [c78]Peter Knees, Tim Pohle, Gerhard Widmer:
Sound/tracks: real-time synaesthetic sonification of train journeys. ACM Multimedia 2008: 1117-1118 - 2007
- [j21]Peter Knees, Markus Schedl, Tim Pohle, Gerhard Widmer:
Exploring Music Collections in Virtual Landscapes. IEEE Multim. 14(3): 46-54 (2007) - [j20]Tim Pohle, Peter Knees, Markus Schedl, Elias Pampalk, Gerhard Widmer:
"Reinventing the Wheel": A Novel Approach to Music Player Interfaces. IEEE Trans. Multim. 9(3): 567-575 (2007) - [c77]Peter Knees, Gerhard Widmer:
Searching for Music Using Natural Language Queries and Relevance Feedback. Adaptive Multimedia Retrieval 2007: 109-121 - [c76]Markus Schedl, Gerhard Widmer:
Automatically Detecting Members and Instrumentation of Music Bands Via Web Content Mining. Adaptive Multimedia Retrieval 2007: 122-133 - [c75]Tim Pohle, Peter Knees, Markus Schedl, Gerhard Widmer:
Building an Interactive Next-Generation Artist Recommender Based on Automatically Derived High-Level Concepts. CBMI 2007: 336-343 - [c74]Fabien Gouyon, Simon Dixon, Gerhard Widmer:
Evaluating Low-Level Features for Beat Classification and Tracking. ICASSP (4) 2007: 1309-1312 - [c73]Søren Tjagvad Madsen, Gerhard Widmer:
Key-Finding with Interval Profiles. ICMC 2007 - [c72]Søren Tjagvad Madsen, Gerhard Widmer:
Towards a Computational Model of Melody Identification in Polyphonic Music. IJCAI 2007: 459-464 - [c71]Tim Pohle, Peter Knees, Markus Schedl, Gerhard Widmer:
Meaningfully Browsing Music Services. ISMIR 2007: 115-116 - [c70]Markus Schedl, Gerhard Widmer, Tim Pohle, Klaus Seyerlehner:
Web-Based Detection of Music Band Members and Line-Up. ISMIR 2007: 117-118 - [c69]Klaus Seyerlehner, Gerhard Widmer, Dominik Schnitzer:
From Rhythm Patterns to Perceived Tempo. ISMIR 2007: 519-524 - [c68]Dominik Schnitzer, Tim Pohle, Peter Knees, Gerhard Widmer:
One-touch access to music on mobile devices. MUM 2007: 103-109 - [c67]Peter Knees, Tim Pohle, Markus Schedl, Gerhard Widmer:
A music search engine built upon audio-based and web-based similarity measures. SIGIR 2007: 447-454 - 2006
- [j19]Alexander K. Seewald, Christian Holzbaur, Gerhard Widmer:
Evaluation of term utility functions for very short multidocument summaries. Appl. Artif. Intell. 20(1): 57-77 (2006) - [j18]Søren Tjagvad Madsen, Gerhard Widmer:
Exploring Pianist Performance Styles with Evolutionary String Matching. Int. J. Artif. Intell. Tools 15(4): 495-514 (2006) - [j17]Asmir Tobudic, Gerhard Widmer:
Relational IBL in classical music. Mach. Learn. 64(1-3): 5-24 (2006) - [j16]Gerhard Widmer:
Guest editorial: Machine learning in and for music. Mach. Learn. 65(2-3): 343-346 (2006) - [j15]Gerhard Widmer:
Guest Editorial: Machine learning in and for music. Mach. Learn. 65(2-3): 347 (2006) - [c66]Markus Schedl, Peter Knees, Tim Pohle, Gerhard Widmer:
Towards Automatic Retrieval of Album Covers. ECIR 2006: 531-534 - [c65]Markus Schedl, Peter Knees, Gerhard Widmer:
Investigating Web-Based Approaches to Revealing Prototypical Music Artists in Genre Taxonomies. ICDIM 2006: 519-524 - [c64]Søren Tjagvad Madsen, Gerhard Widmer:
Separating voices in MIDI. ISMIR 2006: 57-60 - [c63]Arthur Flexer, Fabien Gouyon, Simon Dixon, Gerhard Widmer:
Probabilistic Combination of Features for Music Classification. ISMIR 2006: 111-114 - [c62]Tim Pohle, Peter Knees, Markus Schedl, Gerhard Widmer:
Independent Component Analysis for Music Similarity Computation. ISMIR 2006: 228-233 - [c61]Markus Schedl, Tim Pohle, Peter Knees, Gerhard Widmer:
Assigning and Visualizing Music Genres by Web-based Co-Occurrence Analysis. ISMIR 2006: 260-265 - [c60]Peter Knees, Tim Pohle, Markus Schedl, Gerhard Widmer:
Combining audio-based similarity with web-based data to accelerate automatic music playlist generation. Multimedia Information Retrieval 2006: 147-154 - [c59]Peter Knees, Markus Schedl, Tim Pohle, Gerhard Widmer:
An innovative three-dimensional user interface for exploring music collections enriched. ACM Multimedia 2006: 17-24 - 2005
- [j14]Efstathios Stamatatos, Gerhard Widmer:
Automatic identification of music performers with learning ensembles. Artif. Intell. 165(1): 37-56 (2005) - [j13]Gerhard Widmer:
Musikalisch intelligente Computer Anwendungen in der klassischen und populären Musik. Inform. Spektrum 28(5): 363-368 (2005) - [c58]Markus Schedl, Peter Knees, Gerhard Widmer:
Improving Prototypical Artist Detection by Penalizing Exorbitant Popularity. CMMR 2005: 196-200 - [c57]Elias Pampalk, Arthur Flexer, Gerhard Widmer:
Hierarchical Organization and Description of Music Collections at the Artist Level. ECDL 2005: 37-48 - [c56]Søren Tjagvad Madsen, Gerhard Widmer:
Evolutionary Search for Musical Parallelism. EvoWorkshops 2005: 488-497 - [c55]Søren Tjagvad Madsen, Gerhard Widmer:
Exploring Similarities in Music Performances with an Evolutionary Algorithm. FLAIRS 2005: 80-85 - [c54]Simon Dixon, Werner Goebl, Gerhard Widmer:
The "Air Worm": an Interface for Real-Time manipulation of Expressive Music Performance. ICMC 2005 - [c53]Gerhard Widmer, Simon Dixon, Arthur Flexer, Werner Goebl, Peter Knees, Søren Tjagvad Madsen, Elias Pampalk, Tim Pohle, Markus Schedl, Asmir Tobudic:
The Machine Learning and Intelligent Music Processing Group at the Austrian Research Institute for Artificial Intelligence (öFAI), Vienna. ICMC 2005 - [c52]Asmir Tobudic, Gerhard Widmer:
Learning to Play Like the Great Pianists. IJCAI 2005: 871-876 - [c51]Gerhard Widmer:
Why Computers Need to Learn About Music. ILP 2005: 414 - [c50]Markus Schedl, Peter Knees, Gerhard Widmer:
Discovering and Visualizing Prototypical Artists by Web-Based Co-Occurrence Analysis. ISMIR 2005: 21-28 - [c49]Arthur Flexer, Elias Pampalk, Gerhard Widmer:
Novelty Detection Based on Spectral Similarity of Songs. ISMIR 2005: 260-263 - [c48]Simon Dixon, Gerhard Widmer:
MATCH: A Music Alignment Tool Chest. ISMIR 2005: 492-497 - [c47]Peter Knees, Markus Schedl, Gerhard Widmer:
Multiple Lyrics Alignment: Automatic Retrieval of Song Lyrics. ISMIR 2005: 564-569 - [c46]Elias Pampalk, Arthur Flexer, Gerhard Widmer:
Improvements of Audio-Based Music Similarity and Genre Classificaton. ISMIR 2005: 628-633 - [c45]Elias Pampalk, Tim Pohle, Gerhard Widmer:
Dynamic Playlist Generation Based on Skipping Behavior. ISMIR 2005: 634-637 - [c44]Markus Schedl, Peter Knees, Gerhard Widmer:
Interactive Poster: Using CoMIRVA for Visualizing Similarities Between Music Artists. IEEE Visualization 2005: 89 - 2004
- [j12]Elias Pampalk, Simon Dixon, Gerhard Widmer:
Exploring Music Collections by Browsing Different Views. Comput. Music. J. 28(2): 49-62 (2004) - [j11]Elias Pampalk, Gerhard Widmer, Alvin T. S. Chan:
A new approach to hierarchical clustering and structuring of data with Self-Organizing Maps. Intell. Data Anal. 8(2): 131-149 (2004) - [c43]Gerhard Widmer, Patrick Zanon:
Automatic Recognition of Famous Artists by Machine. ECAI 2004: 1109-1110 - [c42]Craig Saunders, David R. Hardoon, John Shawe-Taylor, Gerhard Widmer:
Using String Kernels to Identify Famous Performers from Their Playing Style. ECML 2004: 384-395 - [c41]Asmir Tobudic, Gerhard Widmer:
Case-Based Relational Learning of Expressive Phrasing in Classical Music. ECCBR 2004: 419-433 - [c40]Simon Dixon, Fabien Gouyon, Gerhard Widmer:
Towards Characterisation of Music via Rhythmic Patterns. ISMIR 2004 - [c39]Peter Knees, Elias Pampalk, Gerhard Widmer:
Artist Classification with Web-Based Data. ISMIR 2004 - 2003
- [j10]Gerhard Widmer:
Discovering simple rules in complex data: A meta-learning algorithm and some surprising musical discoveries. Artif. Intell. 146(2): 129-148 (2003) - [j9]Gerhard Widmer, Simon Dixon, Werner Goebl, Elias Pampalk, Asmir Tobudic:
In Search of the Horowitz Factor. AI Mag. 24(3): 111-130 (2003) - [c38]Asmir Tobudic, Gerhard Widmer:
Playing Mozart Phrase by Phrase. ICCBR 2003: 552-566 - [c37]Asmir Tobudic, Gerhard Widmer:
Relational IBL in Music with a New Structural Similarity Measure. ILP 2003: 365-382 - [c36]Simon Dixon, Elias Pampalk, Gerhard Widmer:
Classification of dance music by periodicity patterns. ISMIR 2003 - [c35]Elias Pampalk, Simon Dixon, Gerhard Widmer:
Exploring music collections by browsing different views. ISMIR 2003 - [c34]Elias Pampalk, Werner Goebl, Gerhard Widmer:
Visualizing changes in the structure of data for exploratory feature selection. KDD 2003: 157-166 - 2002
- [c33]Gerhard Widmer:
In Search of the Horowitz Factor: Interim Report on a Musical Discovery Project. ALT 2002: 41 - [c32]Gerhard Widmer:
In Search of the Horowitz Factor: Interim Report on a Musical Discovery Project. Discovery Science 2002: 13-21 - [c31]Efstathios Stamatatos, Gerhard Widmer:
Music Performer Recognition Using an Ensemble of Simple Classifiers. ECAI 2002: 335-339 - [c30]Marcus-Christopher Ludl, Gerhard Widmer:
Towards a Simple Clustering Criterion Based on Minimum Length Encoding. ECML 2002: 258-269 - [c29]Simon Dixon, Werner Goebl, Gerhard Widmer:
Real Time Tracking and Visualisation of Musical Expression. ICMAI 2002: 58-68 - [c28]Simon Dixon, Werner Goebl, Gerhard Widmer:
The Performance Worm: Real Time Visualisation of Expression based on Langner's Tempo-Loudness Animation. ICMC 2002 - [c27]Björn Bringmann, Stefan Kramer, Friedrich Neubarth, Hannes Pirker, Gerhard Widmer:
Transformation-Based Regression. ICML 2002: 59-66 - 2001
- [j8]Gerhard Widmer:
Using AI and machine learning to study expressive music performance: project survey and first report. AI Commun. 14(3): 149-162 (2001) - [j7]Stefan Kramer, Gerhard Widmer, Bernhard Pfahringer, Michael de Groeve:
Prediction of Ordinal Classes Using Regression Trees. Fundam. Informaticae 47(1-2): 1-13 (2001) - [c26]Gerhard Widmer:
Discovering Strong Principles of Expressive Music Performance with the PLCG Rule Learning Strategy. ECML 2001: 552-563 - [c25]Gerhard Widmer:
The Musical Expression Project: A Challenge for Machine Learning and Knowledge Discovery. ECML 2001: 603-614 - [c24]Alexander K. Seewald, Johann Petrak, Gerhard Widmer:
Hybrid Decision Tree Learners with Alternative Leaf Classifiers: An Empirical Study. FLAIRS 2001: 407-411 - [c23]Gerhard Widmer:
Inductive Learning of General and Robust Local Expression Principles. ICMC 2001 - [c22]Gerhard Widmer:
The Musical Expression Project: A Challenge for Machine Learning and Knowledge Discovery. PKDD 2001: 495-506 - 2000
- [j6]Klaus Kovar, Johannes Fürnkranz, Johann Petrak, Bernhard Pfahringer, Robert Trappl, Gerhard Widmer:
Searching for Patterns in Political Event Sequences: Experiments with the Keds Database. Cybern. Syst. 31(6): 649-668 (2000) - [c21]Marcus-Christopher Ludl, Gerhard Widmer:
Relative Unsupervised Discretization for Regresseion Problems. ECML 2000: 246-253 - [c20]Gerhard Widmer:
Large-scale Induction of Expressive Performance Rules: First Quantitative Results. ICMC 2000 - [c19]Stefan Kramer, Gerhard Widmer, Bernhard Pfahringer, Michael de Groeve:
Prediction of Ordinal Classes Using Regression Trees. ISMIS 2000: 426-434 - [c18]Marcus-Christopher Ludl, Gerhard Widmer:
Relative Unsupervised Discretization for Association Rule Mining. PKDD 2000: 148-158 - [p1]Gerhard Widmer:
On the Potential of Machine Learning for Music Research. Readings in Music and Artificial Intelligence 2000: 69-84
1990 – 1999
- 1998
- [j5]Gerhard Widmer, Miroslav Kubat:
Guest Editors' Introduction. Mach. Learn. 32(2): 83-84 (1998) - 1997
- [j4]Gerhard Widmer:
Tracking Context Changes through Meta-Learning. Mach. Learn. 27(3): 259-286 (1997) - [e1]Maarten van Someren, Gerhard Widmer:
Machine Learning: ECML-97, 9th European Conference on Machine Learning, Prague, Czech Republic, April 23-25, 1997, Proceedings. Lecture Notes in Computer Science 1224, Springer 1997, ISBN 3-540-62858-4 [contents] - 1996
- [j3]Gerhard Widmer, Miroslav Kubat:
Learning in the Presence of Concept Drift and Hidden Contexts. Mach. Learn. 23(1): 69-101 (1996) - [c17]Gerhard Widmer:
What Is It That Makes It a Horowitz? Empirical Musicology via Machine Learning. ECAI 1996: 458-462 - [c16]Gerhard Widmer:
Recognition and Exploitation of Contextual CLues via Incremental Meta-Learning. ICML 1996: 525-533 - 1995
- [c15]Miroslav Kubat, Gerhard Widmer:
Adapting to Drift in Continuous Domains (Extended Abstract). ECML 1995: 307-310 - 1994
- [c14]Gerhard Widmer:
The Synergy of Music Theory and Al: Learning Multi-Level Expressive Interpretation. AAAI 1994: 114-119 - [c13]Gerhard Widmer:
Combining Robustness and Flexibility in Learning Drifting Concepts. ECAI 1994: 468-472 - [c12]Gerhard Widmer:
Learning Expression at Multiple Structural Levels. ICMC 1994 - [c11]Johannes Fürnkranz, Gerhard Widmer:
Incremental Reduced Error Pruning. ICML 1994: 70-77 - 1993
- [j2]Gerhard Widmer, Werner Horn, Bernhard Nagele:
Automatic knowledge base refinement: learning from examples and deep knowledge in rheumatology. Artif. Intell. Medicine 5(3): 225-243 (1993) - [j1]Gerhard Widmer:
Combining Knowledge-Based and Instance-Based Learning to Exploit Qualitative Knowledge. Informatica (Slovenia) 17(4) (1993) - [c10]Gerhard Widmer, Miroslav Kubat:
Effective Learning in Dynamic Environments by Explicit Context Tracking. ECML 1993: 227-243 - [c9]Gerhard Widmer:
Understanding and Learning Musical Expression. ICMC 1993 - 1992
- [c8]Gerhard Widmer, Miroslav Kubat:
Learning Flexible Concepts from Streams of Examples: FLORA 2. ECAI 1992: 463-467 - 1991
- [c7]Gerhard Widmer:
Using Plausible Explanations to Bias Empirical Generalizations in Weak Theory Domains. EWSL 1991: 33-43 - [c6]Werner Horn, Gerhard Widmer, Bernhard Nagele:
Learning Specialized Disease Descriptions in a Rheumatological Expert System. MIE 1991: 322-326 - [c5]Bernhard Nagele, Gerhard Widmer, Werner Horn:
Automatische Verfeinerung der Wissensbasis durch maschinelles Lernen in einem medizinischen Expertensystem. ÖGAI 1991: 68-77 - 1990
- [c4]Gerhard Widmer:
The Usefulness of Qualitative Theories of Musical Perception. ICMC 1990
1980 – 1989
- 1989
- [c3]Gerhard Widmer:
A Tight Integration of Deductive Learning. ML 1989: 11-13 - [c2]Gerhard Widmer:
Wissensbasiertes Lernen in der Musik: Die Integration induktiver und deduktiver Lernmethoden. ÖGAI 1989: 154-163 - 1985
- [c1]Gerhard Widmer, Werner Horn:
VIE-PCX - Ein Expert System Shell für den PC. ÖGAI 1985: 34-41
Coauthor Index
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