Gerhard Widmer



PUBLICATIONS


NOTE: Some of the more recent publications can be downloaded as pdf files from the Research Page of the Department of Computational Perception.

Journals

Cancino Chacón, C., Gadermaier, T., Widmer, G. and Grachten, M. (2017).
An Evaluation of Linear and Non-linear Models of Expressive Dynamics in Classical Piano and Symphonic Music. Machine Learning (Spring 2017), 1-23. DOI: 10.1007/s10994-017-5631-y

Grachten, M., Cancino Chacón, C., Gadermaier, T. and Widmer, G. (2017).
Towards Computer-assisted Understanding of Expressive Dynamics in Symphonic Music.
IEEE Multimedia 24 (1), pp. 36-46.

Widmer, G. (2016).
Getting Closer to the Essence of Music: The Con Espressione Manifesto.
ACM Transactions on Intelligent Systems and Technology 8(2), Article 19. DOI: 10.1145/2899004

Sonnleitner, R. and Widmer, G. (2016).
Robust Quad-based Audio Fingerprinting. IEEE/ACM Transactions on Audio, Speech and Language Processing 24(3), 409-421. Pre-print from IEEE Xplore.

Krebs, F., Holzapfel, A., Cemgil, A.T. and Widmer, G. (2015).
Inferring Metrical Structure in Music Using Particle Filters (pre-print). IEEE/ACM Transactions on Audio, Speech and Language Processing 23(5), 817-827.

Schnitzer, D., Flexer, A., Schedl, M. and Widmer, G. (2012).
Local and Global Scaling Reduce Hubs in Space. Journal of Machine Learning Research 13(Oct), 2871-2902.

Grachten, M. and Widmer, G. (2012).
Linear Basis Models for Prediction and Analysis of Musical Expression. Journal of New Music Research 41(4), 311-322.

Knees, P., Pohle, T. and Widmer, G. (2012).
sound/tracks: Artistic Real-time Sonification of Train Journeys. Journal on Multimodal User Interfaces (in press, online first), Springer, 2012.

Schnitzer, D., Flexer, A., and Widmer, G. (2012).
A Fast Audio Similarity Retrieval Method for Millions of Music Tracks. Multimedia Tools and Applications 58(1), 23-40.

Schedl, M., Pohle, T., Knees, P. and Widmer, G. (2011).
Exploring the Music Similarity Space on the Web. ACM Transactions on Information Systems 29(3).

Schedl, M., Widmer, G., Knees, P. and Pohle, T. (2011).
A Music Information System Automatically Generated via Web Content Mining Techniques. Information Processing & Management vol.47, pp. 426-439.

Flossmann, S., Goebl, W., Grachten, M., Niedermayer, B. and Widmer, G. (2010).
The Magaloff Project: An Interim Report. Journal of New Music Research 39(4), 363-377.

Goebl, W., Flossman, S., and Widmer, G. (2010).
Investigations into between-hand Synchronization in Magaloff's Chopin. Computer Music Journal 34(3), 35-44.

Widmer, G., Flossmann, S., and Grachten, M. (2009).
YQX Plays Chopin. AI Magazine 30(3), 35-48.

Grachten, M., Goebl, W., Flossmann, S. and Widmer, G. (2009).
Phase-plane Representation and Visualization of Gestural Structure in Expressive Timing. Journal of New Music Research 38(2), 183-195.

Saunders, C., Hardoon, D., Shawe-Taylor, J. and Widmer, G. (2008).
Using String Kernels to Identify Famous Performers from their Playing Style. Intelligent Data Analysis 12(4), 425-450.

Widmer, G., Rocchesso, D., Välimäki, V., Erkut, C., Gouyon, F., Pressnitzer, D., Penttinen, H., Polotti, P. and Volpe, G. (2008).
Sound and Music Computing: Research Trends and Some Key Issues. Journal of New Music Research 36(3), 169-184.

Pohle, T., Knees, P., Schedl, M. and Widmer, G. (2007).
"Reinventing The Wheel": A Novel Approach to Music Player Interfaces. IEEE Transactions on Multimedia 9 (3), 567-575.

Knees, P., Schedl, M., Pohle, T., and Widmer, G. (2007).
Exploring Music Collections in Virtual Landscapes. IEEE MultiMedia 14(3), 46-54.

Widmer, G. (Ed.) (2006).
Special Issue on Machine Learning in Music. Machine Learning 65 (2-3), Dec. 2006.

Gouyon, F., Widmer, G., Serra, X. and Flexer, A. (2006).
Acoustic Cues to Beat Induction: A Machine Learning Perspective. Music Perception 24(2), 177-188.

Madsen, S.T. and Widmer, G. (2006).
Exploring Pianist Performance Styles with Evolutionary String Matching. International Journal of Artificial Intelligence Tools, 15(4), 495-514.

Tobudic, A. and Widmer, G. (2006).
Relational IBL in Classical Music. Machine Learning 64:5-24.

Seewald, A.K., Holzbaur, C. and Widmer, G. (2006).
Evaluation of Term Utility Functions for Very Short Multi-Document Summaries. Applied Artificial Intelligence 20(1), 57-77.

Stamatatos, E. and Widmer, G. (2005).
Automatic Identification of Music Performers with Learning Ensembles. Artificial Intelligence 165(1), 37-56.

Widmer, G. (2005).
Studying a Creative Act with Computers: Music Performance Studies with Automated Discovery Methods. Musicae Scientiae IX(1), 11-30.

Schedl, M., Pampalk, E., and Widmer, G. (2005).
Intelligent Structuring and Exploration of Digital Music Collections. e & i - Elektrotechnik und Informationstechnik 7/8, 1-6, Springer Verlag.

Widmer, G. (2005).
Musikalisch intelligente Computer: Anwendungen in der klassischen und populären Musik. Informatik Spektrum 28(5), 363-368.

Knees, P, Pampalk, E., and Widmer, G. (2005).
Automatic Classification of Musical Artists based on Web-Data. ÖGAI Journal 24/1, 16-25.

Pampalk E., Dixon S., and Widmer G. (2004).
Exploring Music Collections by Browsing Different Views. Computer Music Journal 28(2), 49-62.

Pampalk, E., Widmer, G., and Chan, A. (2004).
A New Approach to Hierarchical Clustering and Structuring of Data with Self-Organizing Maps. Intelligent Data Analysis 8(2), 131-149. [ draft (.pdf)]

Widmer, G. and Goebl, W. (2004).
Computational Models of Expressive Music Performance: The State of the Art. Journal of New Music Research 33(3), 203-216.

Widmer, G. (2004).
Artificial Intelligence und Musik: Aktuelle Forschung und Anwendungsperspektiven. OCG Journal 2/2004, 12-14.

Widmer, G. (2003).
Discovering Simple Rules in Complex Data: A Meta-learning Algorithm and Some Surprising Musical Discoveries. Artificial Intelligence 146(2), 129-148. [ rough draft (.pdf, .ps)]

Widmer, G., Dixon, S., Goebl, W., Pampalk, E., and Tobudic, A. (2003).
In Search of the Horowitz Factor. AI Magazine 24(3), 111-130.
(Copyright American Association for Artificial Intelligence (AAAI))

Widmer, G. and Tobudic, A. (2003).
Playing Mozart by Analogy: Learning Multi-level Timing and Dynamics Strategies. Journal of New Music Research 32(3), 259-268.

Widmer, G. (2002).
Machine Discoveries: A Few Simple, Robust Local Expression Principles. Journal of New Music Research 31(1), 37-50. [draft (.pdf, .ps)]

Widmer, G. (2001).
Using AI and Machine Learning to Study Expressive Music Performance: Project Survey and First Report. AI Communications 14(3), 149-162. [draft (.pdf, .ps)]

Kramer, S., Widmer, G., Pfahringer, B., and DeGroeve, M. (2001).
Prediction of Ordinal Classes Using Regression Trees. Fundamenta Informaticae XXI, 1001-1013.

Cambouropoulos, E. and Widmer, G. (2001).
Automatic Motivic Analysis via Melodic Clustering. Journal of New Music Research 29(4), 303-317.

Bresin, R. and Widmer, G. (2000).
Production of Staccato Articulation in Mozart Sonatas Played on a Grand Piano. Preliminary Results. Speech, Music, and Hearing Quarterly Progress and Status Report 4/2000.

Kovar, G., Fürnkranz, J., Petrak, J., Pfahringer, B., Trappl, R., and Widmer, G. (2000).
Searching for Patterns in Political Event Sequences: Experiments with the KEDS Database. Cybernetics and Systems 31(6), 649-668. [draft (.pdf, .ps)]

Widmer, G. and Kubat, M. (Eds.) (1998).
Special Issue on Context Sensitivity and Concept Drift. Machine Learning 32(2).

Widmer, G. (1997).
Tracking Context Changes through Meta-Learning. Machine Learning 27(3), pp.259-286. [very rough draft (.ps)]

Widmer, G. and Kubat, M. (1996).
Learning in the Presence of Concept Drift and Hidden Contexts. Machine Learning 23(1), 69-101. [very rough draft (.ps)]

Widmer, G. (1996).
Learning Expressive Performance: The Structure-Level Approach. Journal of New Music Research 25(2), 179-205.

Widmer, G. (1995).
Modelling the Rational Basis of Musical Expression. Computer Music Journal 19(2), 76-96, MIT Press. [draft (.ps)]

Widmer, G. (1993).
Combining Knowledge-Based and Instance-Based Learning to Exploit Qualitative Knowledge. Informatica 17, 371-385.

Widmer, G., Horn, W., and Nagele, G. (1993).
Automatic Knowledge Base Refinement: Learning from Examples and Deep Knowledge in Rheumatology. Artificial Intelligence in Medicine 5(3), 225-243. [draft (.ps)]

Widmer, G. (1992).
Qualitative Perception Modelling and Intelligent Musical Learning. Computer Music Journal 16(2), 51-68. MIT Press [draft (.ps)]

Books / Book Chapters / Edited Volumes

Collins, T., Arzt, A., Frostel, H. and Widmer, G. (2016).
Using Geometric Symbolic Fingerprinting to Discover Distinctive Patterns in Polyphonic Music Corpora. In D. Meredith (Ed.), Computational Music Analysis. Berlin: Springer Verlag.
Book and original version of chapter available at Springerlink

Serra, X., Magas, M., Benetos, E., Chudy, M., Dixon, S., Flexer, A., Gomez, E., Gouyon, F., Herrera, P., Jorda, S., Paytuvi, O., Peeters, G., Schlüter, J., Vinet, H., and Widmer, G. (2013).
Roadmap for Music Information ReSearch, Creative Commons BY-NC-ND 3.0 license ISBN: 978-2-9540351-1-6. Latest version available on http://mires.eecs.qmul.ac.uk/wiki/index.php/Main_Page

Flossmann, S., Grachten, M. and Widmer, G. (2012).
Expressive Performance Rendering with Probabilistic Models. In Kirke, A. and Miranda, E. (Eds.), Guide to Computing for Expressive Music Performance. New York: Springer Verlag.

Goebl, W. and Widmer, G. (2008).
On the Use of Computational Methods for Expressive Music Performance. In T. Crawford and L. Gibson (Eds.), Modern Methods for Musicology: Prospects, Proposals and Realities. London: Ashgate Publishing.

Bernardini, N., Serra, X., Leman, M. and Widmer, G. (Eds.) (2007).
A Roadmap for Sound and Music Computing. The S2S2 Consortium. (Licensed under Creative Commons).

Widmer, G., Dixon, S., Knees, P., Pampalk, E., and Pohle, T. (2007).
From Sound to "Sense" via Feature Extraction and Machine Learning: Deriving High-level Descriptors for Characterising Music. In P. Polotti and D. Rocchesso (Eds.), Sound to Sense, Sense to Sound -- A State of the Art in Sound and Music Computing. Logos Verlag.

Goebl, W., Dixon, S., De Poli, G., Friberg, A., Bresin, A., and Widmer, G. (2007).
"Sense" in Expressive Music Performance: Data Acquisition, Computational Studies, and Models. In P. Polotti and D. Rocchesso (Eds.), Sound to Sense, Sense to Sound -- A State of the Art in Sound and Music Computing. Logos Verlag.

Kramer, S.  and Widmer, G. (2001).
Inducing Classification and Regression Trees in First-Order Logic. In S. Dzeroski & N. Lavrac (eds.), Relational Data Mining: Inductive Logic Programming for Knowledge Discovery in Databases. Berlin/Heidelberg/New York/Tokyo: Springer Verlag.

Widmer, G. (2000).
On the Potential of Machine Learning for Music Research. In E. Miranda (ed.), Readings in Music and Artificial Intelligence. London: Harwood Academic Publishers. [draft (.ps)]

Birmingham, W.,  Dannenberg, R. and Widmer, G. (eds.) (2000).
AAAI'2000 Workshop on Artificial Intelligence and Music (Workshop Notes), 17th National Conference on Artificial Intelligence (AAAI'2000), Austin, TX.

Widmer, G. (1998).
Applications of Machine Learning to Music Research: Empirical Investigations into the Phenomenon of Musical Expression. In R.S. Michalski, I. Bratko and M. Kubat (eds.), Machine Learning and Data Mining: Methods and Applications, Wiley, Chichester, UK. [draft (.ps)]

van Someren, M. and Widmer, G. (eds.) (1997).
Proceedings of the 9th European Conference on Machine Learning (ECML-97). Springer Verlag, Berlin Heidelberg.

Kubat, M. and Widmer, G. (eds.) (1996).
Learning in Context-Sensitive Domains (Workshop Notes), 13th International Conference on Machine Learning (ICML-96), Bari, Italy.

Widmer, G. (ed.) (1995).
Artificial Intelligence and Music (Workshop Notes). 14th International Joint Conference on Artificial Intelligence (IJCAI-95), Montreal.

Widmer, G. (1994).
Learning with a Qualitative Domain Theory by Means of Plausible Explanations. In R.S. Michalski and G. Tecuci (eds.), Machine Learning: A Multistrategy Approach, Vol.IV. Morgan Kaufmann, San Mateo, CA. [draft (.ps)]

Widmer, G. (1992).
A Knowledge Intensive Approach to Machine Learning in Music. In M. Balaban, K. Ebcioglu, O. Laske (eds.), Understanding Music with AI: Perspectives on Music Cognition. AAAI Press, Menlo Park, CA.

Widmer, G. (ed.) (1992).
Artificial Intelligence and Music (Workshop Notes). 10th European Conference on Artificial Intelligence (ECAI-92), Vienna, Austria.

Refereed Conference Proceedings

Dorfer, M., Kelz, R., and Widmer, G. (2016).
Deep Linear Discriminant Analysis. In Proceedings of the 2nd International Conference on Learning Representations (ICLR 2016), San Juan, Puerto Rico.

Dorfer, M. and Widmer, G. (2016).
Towards Deep and Discriminative Canonical Correlation Analysis. In Proceedings of the ICML 2016 Workshop on Multi-view Representation Learning (MVRL 2016), 33rd International Conference on Machine Learning (ICML 2016), New York, N.Y.

Dorfer, M., Arzt, A. and Widmer, G. (2016).
Towards End-to-End Audio-Sheet-Music Retrieval. In NIPS 2016 Workshop on End-to-end Learning for Speech and Audio Processing. NIPS 2016, Barcelona, Spain.

Eghbal-Zadeh, H., Lehner, B., Dorfer, M. and Widmer, G. (2016).
CP-JKU Submissions for DCASE-2016: A Hybrid Approach Using Binaural i-Vectors and Deep Convolutional Neural Networks. Tech. Report, DCASE (Detection and Classification of Acoustic Scenes and Events) Challenge 2016, July 2016.
Ranks 1 and 2 in DCASE 2016 Audio Scene Classification Challenge.

Eghbal-Zadeh, H., Dorfer, M. and Widmer, G. (2016).
A Cosine-distance Based Neural Network for Music Artist Recognition Using Raw i-Vector Features. In Proceedings of the 19th International Conference on Digital Audio Effects (DAFx-16), Brno, Czech Republic.

Dorfer, M., Arzt, A. and Widmer, G. (2016).
Towards Score Following in Sheet Music Images. In Proceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR 2016), New York, NY.

Sonnleitner, R., Arzt, A. and Widmer, G. (2016).
Landmark-based Audio Fingerprinting for DJ Mix Monitoring. In Proceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR 2016), New York, NY.

Böck, S., Krebs, F. and Widmer, G. (2016).
Joint Beat and Downbeat Tracking with Recurrent Neural Networks. In Proceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR 2016), New York, NY.

Eghbal-Zadeh, H. and Widmer, G. (2016).
Noise-robust Music Artist Recognition Using i-Vector Features. In Proceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR 2016), New York, NY.

Kelz, R., Dorfer, M., Korzeniowski, F., Böck, S., Arzt, A. and Widmer, G. (2016).
On the Potential of Simple Framewise Approaches to Piano Transcription. In Proceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR 2016), New York, NY.

Korzeniowski, F. and Widmer, G. (2016).
A Fully Convolutional Deep Auditory Model for Musical Chord Recognition. In Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2016), Vietro sul Mare, Italy.

Korzeniowski, F. and Widmer, G. (2016).
Feature Learning for Chord Recognition: The Deep Chroma Extractor. In Proceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR 2016), New York, NY.

Krebs, F., Böck, S., Dorfer, M. and Widmer, G. (2016).
Downbeat Tracking Using Beat-synchronous Features with Recurrent Neural Networks. In Proceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR 2016), New York, NY.

Böck, S., Korzeniowski, F., Schlöter, J., Krebs, F. and Widmer, G. (2016).
madmom: A New Python Audio and Music Signal Processing Library. In Proceedings of the 24th ACM International Conference on Multimedia, Amsterdam, The Netherlands. Doi: 10.1145/2964284.2973795.

Arzt, A. and Widmer, G. (2015).
Real-time Music Tracking using Multiple Performances as a Reference. In Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR 2015), Malaga, Spain.
Best Paper Award.

Arzt, A., Frostel, H., Gadermaier, T., Gasser, M., Grachten, M., and Widmer, G. (2015).
Artificial Intelligence in the Concertgebouw. In Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI 2015), Buenos Aires, Argentina.

Eghbal-zadeh, H., Schedl, M. and Widmer, G. (2015).
Timbral Modeling for Music Artist Recognition Using i-Vectors. In Proceedings of the 23th European Signal Processing Conference (EUSIPCO 2015), Nice, France.

Lehner, B., Widmer, G., and Böck, S. (2015).
A Low-latency, Real-time-capable Singing Voice Detection Method with LSTM Recurrent Neural Networks. In Proceedings of the 23th European Signal Processing Conference (EUSIPCO 2015), Nice, France.

Lehner, B., Widmer, G., and Sonnleitner, R. (2015).
Improving Voice Activity Detection in Movies. In Proceedings of INTERSPEECH 2015, Dresden, Germany.

Böck, S., Krebs, F. and Widmer, G. (2015).
Accurate Tempo Estimation based on Recurrent Neural Networks and Resonating Comb Filters. In Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR 2015), Malaga, Spain.

Eghbal-Zadeh, H., Lehner, B., Schedl, M., and Widmer, G. (2015).
I-Vectors for Timbre-Based Music Similarity and Classification. In Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR 2015), Malaga, Spain.

Lehner, B. and Widmer, G. (2015).
Monaural Blind Source Separation in the Context of Singing Voice Detection. In Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR 2015), Malaga, Spain.

Dittmar, C., Lehner, B., Praetzlich, T., Mueller, M. and Widmer, G. (2015).
Cross-Version Singing Voice Detection in Classical Opera Recordings. In Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR 2015), Malaga, Spain.

Gasser, M,. Arzt, A., Gadermaier, T., Grachten, M. and Widmer, G. (2015).
Classical Music on the Web - User Interfaces and Data Representations. In Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR 2015), Malaga, Spain.

Krebs, T., Böck, S. and Widmer, G. (2015).
An Efficient State-Space Model for Joint Tempo and Meter Tracking. In Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR 2015), Malaga, Spain.

Widmer, G. (2014).
What Really Moves Us in Music: Expressivity as a Challenge to Semantic Audio Research. (Abstract) In Proceedings of the 53rd AES Conference on Semantic Audio, Audio Engineering Society. London, UK.

Arzt, A., Böck, S., Flossmann, S., Frostel, H., Gasser, M., and Widmer, G. (2014).
The Complete Classical Music Companion v0.9. In Proceedings of the 53rd AES Conference on Semantic Audio, Audio Engineering Society. London, UK.
Best Demonstration Award.

Collins, T., Böck, S., Krebs, F., and Widmer, G. (2014)
Bridging the Audio-Symbolic Gap: The Discovery of Repeated Note Content Directly from Polyphonic Music Audio. In Proceedings of the 53rd AES Conference on Semantic Audio , Audio Engineering Society. London, UK.
Best Paper Award.

Böck, S., Krebs, R. and Widmer, G. (2014). A Multi-model Approach to Beat Tracking Considering Heterogeneous Music Styles. In Proceedings of the 15th International Society for Music Information Retrieval Conference (ISMIR 2014), Taipei, Taiwan.

Korzeniowski, F., Böck, S. and Widmer, G. (2014). Probabilistic Extraction of Beat Positions from a Beat Activation Function. In Proceedings of the 15th International Society for Music Information Retrieval Conference (ISMIR 2014), Taipei, Taiwan.

Krebs, F., Korzeniowski, F., Grachten, M., and Widmer, G. (2014).
Unsupervised Learning and Refinement of Rhythmic Patterns for Beat and Downbeat Tracking. In Proceedings of the 22nd European Signal Processing Conference (EUSIPCO 2014), Lisbon, Portugal.

Lehner, B., Widmer, G., and Sonnleitner, R. (2014).
On the Reduction of False Positives in Singing Voice Detection. In Proceedings of the 39th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2014), Florence, Italy.

Arzt, A., Böck, S., Flossmann, S., Frostel, H., Gasser, M., Liem, C., and Widmer, G. (2014).
The Piano Music Companion. In Proceedings of the Conference on Prestigious Applications of Intelligent Systems (PAIS 2014 / ECAI 2014), Prague, Czech Republic.
Best Demonstration Award.

Grachten, M., Cancino Chacón, C., and Widmer, G. (2014).
Analysis and Prediction of Expressive Dynamics Using Bayesian Linear Models. In Proceedings of the International Workshop on Computer and Robotic Systems for Automatic Music Performance (SAMP14), Venice, Italy.

Böck, S. and Widmer, G. (2013).
Local Group Delay Aided Vibrato And Tremolo Suppression For Onset Detection. In Proceedings of the 14th International Society for Music Information Retrieval Conference (ISMIR 2013), Curitiba, Brazil.

Collins, T., Arzt, A., Flossmann, S., and Widmer, G. (2013).
SIARCT-CFP: Improving Precision and the Discovery of Inexact Musical Patterns in Point-Set Representations. In Proceedings of the 14th International Society for Music Information Retrieval Conference (ISMIR 2013), Curitiba, Brazil.

Grachten, M., Gasser, M., Arzt, A., and Widmer, G. (2013).
Automatic Alignment of Music Performances with Structural Differences. In Proceedings of the 14th International Society for Music Information Retrieval Conference (ISMIR 2013), Curitiba, Brazil.

Krebs, F., Böck, S., and Widmer, G. (2013)
Rhythmic Pattern Modeling for Beat and Downbeat Tracking in Musical Audio. In Proceedings of the 14th International Society for Music Information Retrieval Conference (ISMIR 2013), Curitiba, Brazil.

Lehner, B., Sonnleitner, R. and Widmer, G. (2013).
Towards Light-weight, Real-time-capable Singing Voice Detection. In Proceedings of the 14th International Society for Music Information Retrieval Conference (ISMIR 2013), Curitiba, Brazil.

Böck, S., Schlüter, J., and Widmer, G. (2013).
Enhanced Peak Picking for Onset Detection with Recurrent Neural Networks. In Proceedings of the 6th International Workshop on Machine Learning and Music, European Conference on Machine Learning (ECML 2013), Prague, Czech Republic.

Gomez, E., Grachten, M., Hanjalic, A., Janer, J., Jorda, J., Julia, C., Liem, C., Martorell, A., Schedl, M. and Widmer, G. (2013).
PHENICX: Performances as Highly Enriched aNd Interactive Concert Experiences- In Proceedings of the Stockholm Music Acoustics Conference (SMAC 2013), Stockholm, Sweden.

Böck, S. and Widmer, G. (2013).
Maximum Filter Vibrato Suppression for Onset Detection. In Proceedings of the 16th International Conference on Digital Audio Effects (DAFx-13), Maynooth, Ireland.

Korzeniowski, F. and Widmer, G. (2013).
Advanced Spectral Template Models for Piano Score Following. In Proceedings of the 10th Sound and Music Computing Conference (SMC 2013), Stockholm, Sweden.

Korzeniowski, F., Krebs, F., Arzt, A. and Widmer, G. (2013).
Tracking Rests and Tempo Changes: Improved Score Following with Particle Filters. In Proceedings of the 2013 International Computer Music Conference (ICMC 2013, Perth, Australia.

Arzt, A., Böck, S. and Widmer, G. (2012).
Fast Identification of Piece and Score Position via Symbolic Fingerprinting. In Proceedings of the 13th International Society for Music Information Retrieval Conference (ISMIR 2012), Porto, Portugal.

Arzt, A., Widmer, G., Böck, S., Sonnleitner, R., and Frostel, H. (2012).
Towards a Complete Classical Music Companion. In Proceedings of the 20th European Conference on Artificial Intelligence (ECAI 2012), Montpellier, France.

Arzt, A., Widmer, G., and Dixon, S. (2012).
Adaptive Distance Mormalization for Real-time Music Tracking. In Proceedings of the 20th European Signal Processing Conference (EUSIPCO 2012) , Bucharest, Romania.

Sonnleitner, R., Niedermayer, B., Widmer, G., and Schl\"uter, J. (2012).
A Simple and Effective Spectral Feature for Speech Detection in Mixed Audio Signals. In Proceedings of the 15th International Conference on Digital Audio Effects (DAFx 2012), York, U.K.

Flossmann, S. and Widmer, G. (2012).
Towards an Evaluation Scheme for Expressive Performance Renderings. In Proceedings of the Workshop on Cross-Disciplinary Perspectives on Expressive Performance, 9th International Symposium on Computer Music Modelling and Retrieval (CMMR 2012), London, U.K.

Krebs, F. and Widmer, G. (2012).
MIREX 2012 Audio Beat Tracking Evaluation: Beat.E. Music Information Retrieval Evaluation eXchange (MIREX) 2012, 13th International Society for Music Information Retrieval Conference (ISMIR 2012), Porto, Portugal.

Grachten, M. and Widmer, G. (2011).
Explaining Musical Expression as a Mixture of Basis Functions. In Proceedings of the 8th Sound and Music Computing Conference (SMC 2011), Padova, Italy.

Grachten, M. and Widmer, G. (2011).
A Method to Determine the Contribution of Annotated Performance Directives in Music Performances. In Proceedings of the International Symposium on Performance Science (ISPS 2011), Toronto. European Association of Conservatoires (AEC), Utrecht, The Netherlands, p. 39-44.

Flossmann, S. and Widmer, G. (2011).
Toward a Multilevel Model of Expressive Piano Performance. In Proceedings of the International Symposium on Performance Science (ISPS 2011), Toronto. European Association of Conservatoires (AEC), Utrecht, The Netherlands.

Flossmann, S. and Widmer, G. (2011).
Toward a Model of Performance Errors: A Qualitative Review of Magaloff's Chopin. In Proceedings of the International Symposium on Performance Science (ISPS 2011), Toronto. European Association of Conservatoires (AEC), Utrecht, The Netherlands.

Niedermayer, B., Böck, S., and Widmer, G. (2011).
On the Importance of 'Real' Audio Data for MIR Algorithm Evaluation at the Note Level - A Comparative Study. In Proceedings of the 12th International Society for Music Information Retrieval Conference (ISMIR 2011), Miami, FL, USA.

Schnitzer, D., Flexer, A., Schedl, M., and Widmer, G. (2011).
Using Mutual Proximity to Improve Content-based Audio Similarity. In Proceedings of the 12th International Society for Music Information Retrieval Conference (ISMIR 2011), Miami, FL, USA..

Niedermayer, B., Widmer, G. and Reuter, C. (2011).
Version Detection for Historical Music Boxes. In Proceedings of the 8th Sound and Music Computing Conference (SMC 2011), Padova, Italy.

Frostel, H., Arzt, A. and Widmer, G. (2011).
The VowelWorm: Real-time Mapping and Visualisation of Sung Vowels in Music. In Proceedings of the 8th Sound and Music Computing Conference (SMC 2011), Padova, Italy.

Holzapfel, A., Flexer, A. and Widmer, G. (2011).
Improving Tempo-sensitive and Tempo-robust Descriptors for Rhythmic Similarity. In Proceedings of the 8th Sound and Music Computing Conference (SMC 2011), Padova, Italy.

Arzt, A. and Widmer, G. (2010).
Simple Tempo Models for Real-time Music Tracking. In Proceedings of the 7th Sound and Music Computing Conference (SMC 2010), Barcelona, Spain.

Niedermayer, B. and Widmer, G. (2010).
Strategies Towards the Automatic Annotation of Classical Piano Music. In Proceedings of the 7th Sound and Music Computing Conference (SMC 2010), Barcelona, Spain.

Seyerlehner, K., Widmer, G., Schedl, M. and Knees, P. (2010).
Automatic Music Tag Classification Based on Block-Level Features. In Proceedings of the 7th Sound and Music Computing Conference (SMC 2010), Barcelona, Spain.

Seyerlehner, K., Widmer, G., and Knees, P. (2010).
A Comparison of Human, Automatic and Collaborative Music Genre Classification and User Centric Evaluation of Genre Classification Systems. In Proceedings of the 8th International Workshop on Multimedia Retrieval (AMR 2010), Linz, Austria.

Seyerlehner, K., Widmer, G. and Pohle, T. (2010).
Fusing Block-Level Features for Music Similarity Estimation. In Proceedings of the 13th International Conference on Digital Audio Effects (DAFx-10), Graz, Austria.

Pohle, T., Knees, P., Seyerlehner, K. and Widmer, G. (2010).
A High-Level Audio Feature for Music Retrieval and Sorting. In Proceedings of the 13th Int. Conference on Digital Audio Effects (DAFx-10), Graz, Austria.

Molina, M., Grachten, M. and Widmer, G. (2010).
Evidence for Pianist-specific Rubato Style in Chopin Nocturnes. In Proceedings of the 11th International Society for Music Information Retrieval Conference (ISMIR 2010), Utrecht, The Netherlands.

Niedermayer, B. and Widmer, G. (2010).
A Multi-Pass Algorithm for Accurate Audio-to-Score Alignment. In Proceedings of the 11th International Society for Music Information Retrieval Conference (ISMIR 2010), Utrecht, The Netherlands.

Flossman, S. Goebl, W. and Widmer, G.(2010).
The Magaloff Corpus: An Empirical Error Study. In Proceedings of the 11th International Conference on Music Perception and Cognition (ICMPC), Seattle, WA, USA.

Knees, P., Schedl, M., Pohle, T., Seyerlehner, K., and Widmer, G. (2010).
Supervised and Unsupervised Web Document Filtering Techniques to Improve Text-Based Music Retrieval. In Proceedings of the 11th International Society for Music Information Retrieval Conference (ISMIR 2010), Utrecht, Netherlands.

Schedl, M., Seyerlehner, K., Schnitzer, D., Widmer, G., and Schiketanz, C. (2010).
Three Web-based Heuristics to Determine a Person's or Institution's Country of Origin. In Proceedings of the 33rd Annual ACM SIGIR Conference (SIGIR 2010), Posters and Demos, Geneva, Switzerland.

Schnitzer, D., Flexer, A., Widmer, G., Gasser, M. (2010).
Islands of Gaussians: The Self Organizing Map and Gaussian Music Similarity Features. In Proceedings of the 11th International Society for Music Information Retrieval Conference (ISMIR 2010), Utrecht, The Netherlands.


Widmer, G. (2009).
Rubinstein in the Phase Plane, Madonna in Feature Space: How AI Will Change the Way We Understand and Deal with Music. (Invited Abstract). In Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI 2009), Pasadena, CA, USA.

Widmer, G. (2009).
Dealing with Music in Intelligent Ways. (Invited Abstract). In Proceedings of the 18th International Symposium on Methodologies for Intelligent Systems (ISMIS 2009), Prague, Czech Republic. Springer Verlag.

Seyerlehner, K., Pohle, T., Widmer, G., and Schnitzer, D. (2009).
Informed Selection of Frames for Music Similarity Computation. In Proceedings of the 12th International Conference on Digital Audio Effects (DAFx-09), Como, Italy.

Flossmann, S., Goebl, W., Niedermayer, B. and Widmer, G. (2009).
Maintaining Skill Across the Life Span: Magaloff's Complete Chopin at Age 77. In Proceedings of the 2nd International Symposium on Performance Science (ISPS 2009), Auckland, New Zealand.

Flossmann, S., Grachten, M., and Widmer, G. (2009).
Expressive Performance Rendering: Introducing Performance Context. In Proceedings of the 6th Sound and Music Computing Conference (SMC 2009), Porto, Portugal.

Goebl, W., Flossmann, S., and Widmer, G. (2009).
Computational Investigations into Between-hand Synchronization in Piano Playing: Magaloff's Complete Chopin. In Proceedings of the 6th Sound and Music Computing Conference (SMC 2009), Porto, Portugal.

Grachten, M. and Widmer, G. (2009).
The Kinematic Rubato Model as a Means of Studying Final Ritards Across Pieces and Pianists. In Proceedings of the 6th Sound and Music Computing Conference (SMC 2009), Porto, Portugal.

Grachten, M. and Widmer, G. (2009).
Who is Who in the End? Recognizing Pianists by their Final Ritardandi. In Proceedings of the 10th International Society for Music Information Retrieval Conference (ISMIR 2009), Kobe, Japan.

Grachten, M., Schedl, M., Pohle, T., and Widmer, G. (2009).
The ISMIR Cloud: A Decade of ISMIR Conferences at Your Fingertips. In Proceedings of the 10th International Society for Music Information Retrieval Conference (ISMIR 2009), Kobe, Japan.

Knees, P., Pohle, T., Schedl, M., Schnitzer, D., Seyerlehner, K., and Widmer, G. (2009).
Augmenting Text-based Music Retrieval with Audio Similarity. In Proceedings of the 10th International Society for Music Information Retrieval Conference (ISMIR 2009), Kobe, Japan.

Pohle, T., Schnitzer, D., Schedl, M., Knees, P. and Widmer, G. (2009).
On Rhythm and General Music Similarity. In Proceedings of the 10th International Society for Music Information Retrieval Conference (ISMIR 2009), Kobe, Japan.

Seyerlehner, K., Flexer, A., and Widmer, G. (2009).
On the Limitations of Browsing Top-N Recommender Systems. In Proceedings of the 3rd ACM Conference on Recommender Systems (RecSys'09), New York, N.Y.

Schnitzer, D., Flexer, A., and Widmer, G. (2009).
A Filter-and-Refine Indexing Method for Fast Similarity Search in Millions of Music Tracks. In Proceedings of the 10th International Society for Music Information Retrieval Conference (ISMIR 2009), Kobe, Japan.

Seyerlehner, K., Knees, P., Schnitzer, D., and Widmer, G. (2009).
Browsing Music Recommendation Networks. In Proceedings of the 10th International Society for Music Information Retrieval Conference (ISMIR 2009), Kobe, Japan.

Arzt, A., Widmer, G., and Dixon, S. (2008).
Automatic Page Turning for Musicians via Real-Time Machine Listening. In Proceedings of the 18th European Conference on Artificial Intelligence (ECAI 2008), Patras, Greece.

Pohle, T., Knees, P., and Widmer, G. (2008).
sound/tracks: Real-Time Synaesthetic Sonification and Visualisation of Passing Landscapes. In Proceedings of the ACM Multimedia 2008 - Interactive Arts Program, Vancouver, BC, Canada.

Knees, P., Pohle, T., and Widmer, G. (2008).
sound/tracks: Real-Time Synaesthetic Sonification of Train Journeys. In Proceedings of the ACM Multimedia 2008 - Interactive Arts Exhibition, Vancouver, BC, Canada.

Flexer, A., Schnitzer, D., Gasser, M., and Widmer, G. (2008).
Playlist Generation Using Start and End Songs. In Proceedings of the 9th International Conference on Music Information Retrieval (ISMIR 2008), Philadelphia, PA.

Gasser, M., Flexer, A., and Widmer, G. (2008).
StreamCatcher - Integrated Visualization of Music Clips and Online Audio Streams. In Proceedings of the 9th International Conference on Music Information Retrieval (ISMIR 2008), Philadelphia, PA.

Gasser, M., Schnitzer, D., Flexer, A., and Widmer, G. (2008).
FM4 Soundpark: Audio-based Music Recommendation in Everyday Use. Demonstration Session, 9th International Conference on Music Information Retrieval (ISMIR 2008), Philadelpha, PA.

Grachten, M., Goebl, W., Flossmann, S., and Widmer, G. (2008).
Intuitive Visualization of Expressive Timing: A Case Study on the Final Ritard. In Proceedings of the 10th International Conference on Music Perception and Cognition (ICMPC 2008), Sapporo, Japan.

Flossmann, S., Grachten, M., and Widmer, G. (2008).
Experimental Investigations into the Use of Score Features for Computational Models of Expressive Timing. In Proceedings of the 10th International Conference on Music Perception and Cognition (ICMPC 2008), Sapporo, Japan.

Seyerlehner, K., Widmer, G., and Knees, P. (2008).
Frame-level Audio Similarity - A Codebook Approach. In Proceedings of the 11th International Conference on Digital Audio Effects (DAFx-08), Espoo, Finland.

Madsen, S.T., Typke, R. and Widmer, G. (2008).
Automatic Reduction of MIDI Files Preserving Relevant Musical Content. In Proceedings of the 6th Workshop on Adaptive Multimedia Retrieval (AMR 2008), Berlin, Germany.

Pohle, T., Seyerlehner, K., and Widmer, G. (2008).
An Approach to Automatically Tracking Music Preference on Mobile Players. In Proceedings of the 6th Workshop on Adaptive Multimedia Retrieval (AMR 2008), Berlin, Germany.

Grachten, M., Goebl, W., Flossmann, S., and Widmer, G. (2008).
Phase-plane Visualizations of Gestural Structure in Expressive Timing. In Proceedings of the Fourth Conference on Interdisciplinary Musicology (CIM08) Thessaloniki, Greece.

Schedl, M., Knees, P., Pohle, T., and Widmer, G. (2008).
Towards an Automatically Generated Music Information System via Web Content Mining. In Proceedings of the 30th European Conference on Information Retrieval (ECIR 2008), Glasgow, Scotland, UK.

Schnitzer, D., Pohle, T., Knees, P. and Widmer, G. (2007).
One-Touch Access to Music on Mobile Devices. In Proceedings of the 6th International Conference on Mobile and Ubiquitous Multimedia (MUM 2007), Oulu, Finland.
(Best Student Paper Award.)

Knees, P., Pohle, T., Schedl, M. and Widmer, G. (2007).
A Music Search Engine Built upon Audio-based and Web-based Similarity Measures. In Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'07), Amsterdam, the Netherlands.

Madsen, S. T. and Widmer, G. (2007).
Towards a Computational Model of Melody Identification in Polyphonic Music. In Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI 2007), Hyderabad, India.

Seyerlehner, K., Widmer, G., Pohle, T. and Schedl, M. (2007).
Automatic Music Detection in Television Productions. In Proceedings of the International Conference on Digital Audio Effects (DAFx 07), Bordeaux, France.

Grachten, M. and Widmer, G. 2007).
Towards Phrase Structure Reconstruction from Expressive Performance Data. In Proceedings of the International Conference on Music Communication Science (ICOMCS), Sydney, Australia.

Madsen, S.T. and Widmer, G. (2007).
Key Finding with Interval Profiles. In Proceedings of the International Computer Music Conference (ICMC 2007), Copenhagen, Denmark.

Seyerlehner, K., Widmer, G., and Schnitzer, D. (2007).
From Rhythm Patterns to Perceived Tempo. In Proceedings of the 8th International Conference on Music Information Retrieval (ISMIR 2007), Vienna, Austria.

Schedl, M., Widmer, G., Pohle, T. and Seyerlehner, K. (2007).
Web-based Detection of Music Band Members and Line-Up. In Proceedings of the 8th International Conference on Music Information Retrieval (ISMIR 2007), Vienna, Austria.

Pohle, T., Knees, P., Schedl, M., and Widmer, G. (2007).
Meaningfully Browsing Music Services. In Proceedings of the 8th International Conference on Music Information Retrieval (ISMIR'07), Vienna, Austria.

Schedl, M., Knees, P., Widmer, G., Seyerlehner, K. and Pohle, T. (2007).
Browsing the Web Using Stacked Three-Dimensional Sunbursts to Visualize Term Co-Occurrences and Multimedia Content. In Proceedings of the 18th IEEE Visualization 2007 Conference (Vis'07), Sacramento, California.

Gouyon, F., Dixon, S., and Widmer, G. (2007).
Evaluating Low-level Features for Beat Classification and Tracking. In Proceedings of the 32nd International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2007), Honolulu, Hawaii.

Knees, P. and Widmer, G. (2007).
Searching for Music using Natural Language Queries and Relevance Feedback. In Proceedings of the 5th Workshop on Adaptive Multimedia Retrieval (AMR'07), Paris, France.

Schedl, M. and Widmer, G. (2007).
Automatically Detecting Members and Instrumentation of Music Bands via Web Content Mining. In Proceedings of the 5th Workshop on Adaptive Multimedia Retrieval (AMR'07), Paris, France.

Pohle, T., Knees, P., Schedl, M. and Widmer, G. (2007).
Building an Interactive Next-Generation Artist Recommender Based on Automatically Derived High-Level Concepts. In Proceedings of the 5th International Workshop on Content Based Multimedia Indexing (CBMI'07), Bordeaux, France.

Madsen, S.T. and Widmer, G. (2007).
A Complexity-based Approach to Melody Track Identification in MIDI Files. In Proceedings of the International Workshop on Music and Artificial Intelligence, 20th International Joint Conference on Artificial Intelligence (IJCAI-07), Hyderabad, India.


Schedl, M., Knees, P. and Widmer, G. (2006).
Investigating Web-Based Approaches to Revealing Prototypical Music Artists in Genre Taxonomies. In Proceedings of the 1st IEEE International Conference on Digital Information Management (ICDIM'06), Bangalore, India, December 2006.

Knees, P., Schedl, M., Pohle, T. and Widmer, G. (2006).
An Innovative Three-Dimensional User Interface for Exploring Music Collections Enriched with Meta-Information from the Web. In Proceedings of the ACM Multimedia 2006, Santa Barbara, California, USA, October 2006.

Knees, P., Pohle, T., Schedl, M. and Widmer, G. (2006).
Combining Audio-based Similarity with Web-based Data to Accelerate Automatic Music Playlist Generation. In Proceedings of the 8th ACM SIGMM International Workshop on Multimedia Information Retrieval (MIR'06), Santa Barbara, California, USA, October 2006.

Knees, P., Pohle, T., Schedl, M., and Widmer, G. (2006).
Automatically Describing Music on a Map. In Proceedings of the 1st Workshop on Learning the Semantics of Audio Signals (LSAS 2006), 1st International Conference on Semantics and Digital Media Technology (SAMT 2006), Athens, Greece.

Pohle, T., Schedl, M., Knees, P., and Widmer, G. (2006).
Automatically Adapting the Structure of Audio Similarity Spaces. In Proceedings of the 1st Workshop on Learning the Semantics of Audio Signals (LSAS 2006), 1st International Conference on Semantics and Digital Media Technology (SAMT 2006), Athens, Greece.

Goebl, W. and Widmer, G. (2006).
Unobtrusive Practise Tools for Pianists. In 9th International Conference on Music Perception and Cognition (ICMPC 2006), Bologna, Italy.

Madsen, S. T. and Widmer, G. (2006).
Music Complexity Measures Predicting the Listening Experience. In 9th International Conference on Music Perception and Cognition (ICMPC 2006), Bologna, Italy.

Schedl, M., Knees, P., and Widmer, G. (2006).
Towards Automatic Retrieval of Album Covers. In Proceedings of the 28th European Conference on Information Retrieval (ECIR 2006), London, U.K.

Flexer, A., Gouyon, F., Dixon, S. and Widmer, G. (2006).
Probabilistic Combination of Features for Music Classification. In Proceedings of the 7th International Conference on Music Information Retrieval (ISMIR 2006), Victoria, Canada.

Madsen, S.T. and Widmer, G. (2006).
Separating Voices in MIDI. In Proceedings of the 7th International Conference on Music Information Retrieval (ISMIR 2006), Victoria, Canada.

Pohle, T., Knees, P., Schedl, M. and Widmer, G. (2006).
Independent Component Analysis for Music Similarity Computation. In Proceedings of the 7th International Conference on Music Information Retrieval (ISMIR 2006), Victoria, Canada.

Schedl, M., Pohle, T., Knees, P. and Widmer, G. (2006).
Assigning and Visualizing Music Genres by Web-based Co-occurrence Analysis. In Proceedings of the 7th International Conference on Music Information Retrieval (ISMIR 2006), Victoria, Canada.

Trajano de Lima, E., Madsen, S.T., Dahia, M., Widmer, G., and Ramalho, G. (2006).
Extracting Patterns from Brazilian Guitar Accompaniment Data. In Proceedings of the First European Workshop on Intelligent Technologies for Cultural Heritage Exploitation, 17h European Conference on Artificial Intelligence (ECAI 2006), Riva del Garda, Italy.


Tobudic, A. and Widmer, G. (2005).
Learning to Play Like the Great Pianists. In Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI'05), Edinburgh, Scotland.

Pampalk, E., Flexer, A., and Widmer, G. (2005).
Hierarchical Organization and Description of Music Collections at the Artist Level. In Proceedings of the 9th European Conference on Research and Advanced Technology for Digital Libraries (ECDL 2005), Vienna, Austria.

Dixon, S., Goebl, W., and Widmer, G. (2005).
The "Air Worm": An Interface for Real-time Manipulation of Expressive Music Performance. In Proceedings of the International Computer Music Conference (ICMC 2005), Barcelona, Spain. San Francisco, CA: International Computer Music Association.

Trajano de Lima, E., Madsen, S.T., Dahia, M., Widmer, G., and Ramalho, G. (2005).
Extracting Patterns from Guitar Accompaniment Data: Some Experimental Results. In Proceedings of the 10th Brazilian Symposium on Computer Music (SBCM 2005), Belo Horizonte, Brazil.

Widmer, G., Dixon, S., Flexer, A., Goebl, W., Knees, P., Madsen, S., Pampalk, E., Pohle, T., Schedl, M., and Tobudic, A. (2005).
Studio Report: The Machine Learning and Intelligent Music Processsing Group at the Austrian Reserach Institute for Artificial Intelligence (ÖFAI), Vienna. In Proceedings of the International Computer Music Conference (ICMC 2005) , Barcelona, Spain. San Francisco, CA: International Computer Music Association.

Flexer, A., Pampalk, E., and Widmer, G. (2005).
Hidden Markov Models for Spectral Similarity of Songs. In Proceedings of the 8th International Conference on Digital Audio Effects (DAFx 2005), Madrid, Spain.

Pohle, T., Pampalk, E., and Widmer, G. (2005).
Generating Similarity-based Playlists Using Traveling Salesman Algorithms. In Proceedings of the 8th International Conference on Digital Audio Effects (DAFx 2005), Madrid, Spain.

Knees, P., Schedl, M. and Widmer, G. (2005).
Multiple Lyrics Alignment: Automatic Retrieval of Song Lyrics. In Proceedings of the 6th International Conference on Music Information Retrieval (ISMIR 2005), London, UK.

Schedl, M., Knees, P., and Widmer, G. (2005).
Using CoMIRVA for Visualizing Similarities Between Music Artists. In Proceedings of the 16th IEEE Visualization Conference (Vis 2005), Minneapolis, MN.

Schedl, M., Knees, P. and Widmer, G. (2005).
Discovering and Visualizing Prototypical Artists by Web-based Co-occurrence Analysis. In Proceedings of the 6th International Conference on Music Information Retrieval (ISMIR 2005), London, UK.

Dixon, S. and Widmer, G. (2005).
MATCH: A Music Alignment Tool Chest. In Proceedings of the 6th International Conference on Music Information Retrieval (ISMIR 2005), London, UK. (ISMIR 05 Best Poster Award.)

Flexer, A., Pampalk, E. and Widmer, G. (2005).
Novelty Detection for Spectral Similarity of Songs. In Proceedings of the 6th International Conference on Music Information Retrieval (ISMIR 2005), London, UK.

Pampalk, E., Flexer, A. and Widmer, G. (2005).
Improvements of Audio-based Music Similarity and Genre Classification. In Proceedings of the 6th International Conference on Music Information Retrieval (ISMIR 2005), London, UK.

Pampalk, E., Pohle, T. and Widmer, G. (2005).
Dynamic Playlist Generation based on Skipping Behavior. In Proceedings of the 6th International Conference on Music Information Retrieval (ISMIR 2005), London, UK.

Schedl, M., Knees, P., and Widmer, G. (2005).
Improving Prototypical Artist Detection by Penalizing Exorbitant Popularity. In Proceedings of the 3rd International Conference on Computer Music Modeling and Retrieval (CMMR 2005), Pisa, Italy. Berlin: Springer Verlag.

Herrera,P., Bello, J., Widmer, G., Sandler, M., Celma, O., Vignol, F., Pampalk, E., Cano, P., Pauws, S. and Serra X. (2005).
SIMAC: Semantic Interaction with Music Audio Contents. In Proceedings of the 2nd European Workshop on the Integration of Knowledge, Semantic and Digital Media Technologies (EWIMT 2005), London, U.K.

Schedl, M., Knees, P., and Widmer, G. (2005).
A Web-Based Approach to Assessing Artist Similarity using Co-Occurrences. In Proceedings of the Fourth International Workshop on Content-Based Multimedia Indexing (CBMI'05), Riga, Latvia.

Pohle, T., Pampalk, E., and Widmer, G. (2005).
Evaluation of Frequently Used Audio Features for Classification of Music into Perceptual Categories. In Proceedings of the Fourth International Workshop on Content-Based Multimedia Indexing (CBMI'05), Riga, Latvia.

Madsen, S. T. and Widmer, G. (2005).
Exploring Similarities in Music Performances with an Evolutionary Algorithm. In Proceedings of the 18th International FLAIRS Conference (FLAIRS'05), Clearwater Beach, Florida. AAAI Press.

Madsen, S. T. and Widmer, G. (2005).
Evolutionary Search for Musical Parallelism. In Proceedings of the 3rd European Workshop on Evolutionary Music and Art (EvoMUSART'2005), Lausanne, Switzerland. Berlin: Springer Verlag.

Gouyon, F. and Widmer, G. (2005).
Acoustic Cues to Beat Induction: A Machine Learning Perspective. (Abstract) 10th Rhythm Perception and Production Workshop, Bilzen, Belgium.

Widmer, G. (2005).
Why Computers Need to Learn About Music (Abstract). In Proceedings of the 15th International Conference on Inductive Logic Programming (ILP 2005), Bonn, Germany. Berlin: Springer Verlag.

Gstrein, E., Kleedorfer, F., Mayer, R., Schmotzer, C., Widmer, G., Holle, O., and Miksch, S. (2005).
Adaptive Personalization: A Multi-Dimensional Approach to Boosting a Large Scale Mobile Music Portal. Fifth Open Workshop on MUSICNETWORK: Integration of Music in Multimedia Applications, Vienna, Austria.

Saunders C., Hardoon D., Shawe-Taylor J., and Widmer G. (2004).
Using String Kernels to Identify Famous Performers from their Playing Style, Proceedings of the 15th European Conference on Machine Learning (ECML'2004), Pisa, Italy, 2004.
(ECML'2004 Best Paper Award)

Dixon, S., Gouyon, F., and Widmer, G. (2004).
Towards Characterisation of Music via Rhythmic Patterns. In Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR'2004), Barcelona, Spain.

Knees, P., Pampalk, E., and Widmer, G. (2004).
Artist Classification with Web-based Data. In Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR'2004), Barcelona, Spain.

Gouyon, F., Dixon, S., Pampalk, E. and Widmer, G. (2004).
Assessing the Relevance of Rhythmic Descriptors in a Musical Genre Classification Task. Proceedings of the AES 25th International Conference, Audio Engineering Society, London.

Goebl, W., Pampalk, E., and Widmer, G. (2004).
Exploring Expressive Performance Trajectories: Six Famous Pianists Play Six Chopin Pieces. Proceedings of the  8th International Conference on Music Perception and Cognition (ICMPC'04), Evanston, Illinois.

Tobudic, A. and Widmer, (2004).
A Relational Approach to Learning Expressive Phrasing in Classical Music. In Proceedings of the 7th European Conference on Case-Based Reasoning (ECCBR'2004), Madrid, Spain.

Widmer, G. (2004).
Intelligent Computing for Music and Musicology. In Proceedings of the First Central European International Multimedia and Virtual Reality Conference (CEIMVRC04), Veszprém, Hungary.

Widmer, G. and Zanon, P. (2004).
Automatic Recognition of Famous Artists by Machine, Proceedings of the 16th European Conference on Artificial Intelligence (ECAI'2004), Valencia, Spain.
[extended version available online at http://www.ofai.at/cgi-bin/tr-online?number+2004-04]

Pampalk, E., Goebl, E., and Widmer, G. (2003).
Visualizing Changes in the Structure of Data for Exploratory Feature Selection. In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2003), Washington, D.C.

Pampalk, E., Dixon, S., and Widmer, G. (2003).
On the Evaluation of Perceptual Similarity Measures for Music. In Proceedings of the 6th International Conference on Digital Audio Effects (DAFx-03), London.

Dixon, S., Pampalk, E., and Widmer, G. (2003).
Classification of Dance Music by Periodicity Patterns. In Proceedings of the Fourth International Conference on Music Information Retrieval (ISMIR'2003), Washington, D.C.

Pampalk, E., Dixon, S., and Widmer, G. (2003).
Exploring Music Collections by Browsing Different Views. In Proceedings of the Fourth International Conference on Music Information Retrieval (ISMIR'2003), Washington, D.C.

Tobudic, A. and Widmer, G. (2003).
Relational IBL in Music with a New Structural Similarity Measure. In Proceedings of the 13th International Conference on Inductive Logic Programming (ILP 2003), Szeged, Hungary. Berlin: Springer Verlag.

Tobudic, A. and Widmer, G. (2003).
Playing Mozart Phrase by Phrase. In Proceedings of the 5th International Conference on Case-based Reasoning (ICCBR'03), Trondheim, Norway. [draft (.pdf, .ps)]

Tobudic, A. and Widmer, G. (2003).
Learning to Play Mozart: Recent Improvements. In Proceedings of the IJCAI'03 Workshop on Methods for Automatic Music Performance and their Applications in a Public Rendering Contest. 18th Joint International Conference on Artificial Intelligence (IJCAI'03), Acapulco, Mexico.

Widmer, G. (2003).
Large-scale Performance Studies with Intelligent Data Analysis Methods. In Proceedings of the 3rd Decennial Stockholm Music Acoustics Conference (SMAC'03), Stockholm, Sweden.

Zanon, P. and Widmer, G. (2003).
Learning to Recognize Famous Pianists with Machine Learning Techniques. In Proceedings of the 3rd Decennial Stockholm Music Acoustics Conference (SMAC'03), Stockholm, Sweden.

Zanon, P. and Widmer, G. (2003).
Recognition of Famous Pianists Using Machine Learning Algorithms: First Experimental Results. In Proceedings of the 14th Colloquium on Musical Informatics (XIV CIM 2003), Florence, Italy. [draft (.pdf, .ps)]

Widmer, G. (2002).
In Search of the Horowitz Factor: Interim Report on a Musical Discovery Project. Invited paper. In Proceedings of the 5th International Conference on Discovery Science (DS'02), Lübeck, Germany. Berlin: Springer Verlag. [draft (.pdf, .ps)]

Bringmann, B., Kramer, S., Neubarth, F., Pirker, H., and Widmer, G. (2002).
Transformation-based Regression. In Proceedings of the 19th International Conference on Machine Learning (ICML'2002), Sydney, Australia. [draft (.pdf, .ps)]

Stamatatos, E. and Widmer, G. (2002).
Music Performer Recognition Using an Ensemble of Simple Classifiers. In Proceedings of the 15th European Conference on Artificial Intelligence (ECAI'2002), Lyon, France. [draft (.pdf, .ps)]

Ludl, M. and Widmer, G. (2002).
Towards a Simple Clustering Criterion Based on Minimum Length Encoding. In Proceedings of the 13th European Conference on Machine Learning (ECML'02), Helsinki, Finland. Berlin: Springer Verlag. [draft (.pdf, .ps)]

Dixon, S., Goebl, W., and Widmer, G. (2002).
The Performance Worm: Real Time Visualisation of Expression Based on Langner's Tempo-Loudness Animation. In Proceedings of the International Computer Music Conference (ICMC'2002), Göteborg, Sweden. [draft (.pdf, .ps)]

Dixon, S., Goebl, W., and Widmer, G. (2002).
Real Time Tracking and Visualisation of Musical Expression. In Proceedings of the 2nd International Conference on Music and Artificial Intelligence (ICMAI'2002), Edinburgh, Scotland. [draft (.pdf, .ps)]

Widmer, G. and Tobudic, A. (2002).
Playing Mozart by Analogy: Learning Multi-level Timing and Dynamics Strategies. In Proceedings of the ICAD Workshop on Performance Rendering Systems, 8th International Conference on Auditory Display (ICAD'2002), Kyoto, Japan. [draft (.pdf, .ps)]

Widmer, G. (2002).
Trying to Explain a Creative Act: Studying Expressive Music Performance with Learning Machines. In Proceedings of the ESCOM Conference on Musical Creativity, Liège, Belgium.

Widmer, G. (2001).
The Musical Expression Project: A Challenge for Machine Learning and Knowledge Discovery. Invited talk / paper. In Proceedings of the 12th European Conference on Machine Learning (ECML'01), and in Proceedings of the 5th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'01), Freiburg. Berlin: Springer Verlag. [draft (.pdf, .ps)]

Widmer, G. (2001).
Discovering Strong Principles of Expressive Music Performance with the PLCG Rule Learning Strategy. In Proceedings of the 12th European Conference on Machine Learning (ECML'01), Freiburg. Berlin: Springer Verlag. [draft (.pdf, .ps)]

Seewald, A., Petrak, J., and Widmer, G. (2001).
Hybrid Decision Tree Learners with Alternative Leaf Classifiers: An Empirical Study. In Proceedings of the 14th International FLAIRS Conference (FLAIRS'2001), Key West, Florida. [draft (.pdf, .ps)]

Widmer, G. (2001).
Inductive Learning of General and Robust Local Expression Principles. In Proceedings of the International Computer Music Conference (ICMC'2001), La Habana, Cuba.

Cambouropoulos, E., Dixon, S., Goebl, W., and Widmer, G. (2001).
Computational Models of Tempo: Comparison of Human and Computer Beat-tracking. Proceedings of the VII International Symposium on Systematic and Comparative Musicology, Jyväskylä, Finland. [draft (.pdf, .ps)]

Widmer, G., Dixon, S., Goebl, W., Stamatatos, E., and Tobudic, A. (2001).
Empirical Music Performance: ÖFAI's Position. Panel Discussion Paper, MOSART Workshop on Current Research Directions in Computer Music, Barcelona. 

Ludl, M. and Widmer, G. (2000).
Relative Unsupervised Discretization for Regression Problems. In Proceedings of the 11th European Conference on Machine Learning (ECML'2000), Barcelona. Springer Verlag, Berlin. [draft (.pdf, .ps)]

Ludl, M. and Widmer, G. (2000).
Relative Unsupervised Discretization for Association Rule Mining. In Proceedings of the 4th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'2000), Lyon, France. Springer Verlag, Berlin. [draft (.pdf, .ps)]

Kramer, S., Widmer, G., Pfahringer, B., and de Groeve, M. (2000).
Prediction of Ordinal Classes Using Regression Trees. In Proceedings of the 12th International Symposium on Methodologies for Intelligent Systems (ISMIS'2000), Charlotte, N.C.

Widmer, G. (2000).
Large-scale Induction of Expressive Performance Rules: First Quantitative Results. In Proceedings of the International Computer Music Conference (ICMC'2000), Berlin. [draft (.pdf, .ps)]

Widmer, G. (2000).
Learning about Musical Expression via Machine Learning: A Status Report. In W. Birmingham, R. Dannenberg, & G. Widmer (eds.), AAAI'2000 Workshop on Artificial Intelligence and Music (Workshop Notes), 17th National Conference on Artificial Intelligence (AAAI'2000), Austin, TX.

Cambouropoulos, E. and Widmer, G. (2000).
Melodic Clustering: Motivic Analysis of Schumann's Träumerei. In Proceedings of Journées d'Informatique Musicale (JIM'2000), Bordeaux, 15-18 May 2000. [draft (.pdf, .ps)]

Cambouropoulos, E., Smaill, A., and Widmer, G. (1999).
A Clustering Algorithm for Melodic Analysis. In Proceedings of the Diderot Forum on Mathematics and Music, Vienna, Austria. [draft (.pdf, .ps)]

Widmer, G. (1997).
Learning in Dynamically Changing Domains: Recent Contributions of Machine Learning. In Proceedings of the MLNet Workshop on Learning in Dynamically Changing Domains: Theory Revision and Context Dependence Issues, Prague, Czech Republic, 1997. [draft (.ps)]

Widmer, G. (1996).
Recognition and Exploitation of Contextual Clues via Incremental Meta-Learning. In Proceedings of the 13th International Conference on Machine Learning (ML-96). Morgan Kaufmann, San Francisco, CA. [draft (.ps)]

Widmer, G. (1996).
What Is It That Makes It a Horowitz? Empirical Musicology via Machine Learning. In Proceedings of the 12th European Conference on Artificial Intelligence (ECAI-96). Wiley & Sons, Chichester, UK.

Widmer, G. (1996).
On-line Metalearning in Changing Contexts: MetaL(B) and MetaL(IB). In Proceedings of the 3rd International Workshop on Multistrategy Learning (MSL-96). AAAI Press, Menlo Park, CA.

Widmer, G. (1996).
Recent Developments in Context-Sensitive Learning: The MetaL(.) Family of Meta-Learners. In Proceedings of the Third International Workshop on Artificial Intelligence Techniques (AIT'96), Brno, Czech Republic.

Kubat, M. and Widmer, G. (1995).
Learning Time-varying Decisions: Interim Summary of the FLORA Methodology. In Proceedings of the Second International Workshop on Artificial Intelligence Techniques (AIT'95), Brno, Czech Republic.

Kubat, M. and Widmer, G. (1995).
Adapting to Drift in Continuous Domains (Extended Abstract). In N. Lavrac and S. Wrobel (eds.), Proceedings of the 8th European Conference on Machine Learning (ECML-95), Springer, Berlin Heidelberg New York, pp. 307-310. [draft (.ps)]

Widmer, G. (1995).
A Machine Learning Analysis of Expressive Timing in Pianists' Performances of Schumann's "Träumerei". In A. Friberg and J. Sundberg (ed.), Proceedings of the KTH Symposium on Generative Grammars for Music Performance, Royal Institute of Technology (KTH), Stockholm, Sweden. [draft (.ps)]

Widmer, G. (1994).
Combining Robustness and Flexibility in Learning Drifting Concepts. In A.G. Cohn (ed.), Proceedings of the 11th European Conference on Artificial Intelligence (ECAI94), Wiley, Chichester, UK, pp.468-472. [draft (.ps)]

Fürnkranz, J. and Widmer, J. (1994).
Incremental Reduced Error Pruning. In Proceedings of the 11th International Conference on Machine Learning (ICML-94), Morgan Kaufmann, San Francisco, CA. [draft (.ps)]

Widmer, G. (1994).
The Synergy of Music Theory and AI: Learning Multi-Level Expressive Interpretation. In Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI-94). AAAI Press/MIT Press, Cambridge, MA, pp.114-119. [draft (.ps)]

Widmer, G. (1994).
Learning Expression at Multiple Structural Levels. In Proceedings of the International Computer Music Conference (ICMC-94), Aarhus, Denmark. San Francisco, CA: International Computer Music Association.

Widmer, G. (1994).
Studying Musical Expression with AI and Machine Learning: "Analysis by Resynthesis". In J. Sundberg (ed.), Proceedings of the Aarhus Symposium on Generative Grammars for Music Performance. Royal Institute of Technology (KTH), Stockholm, Sweden.

Widmer, G. (1993).
Plausible Explanations and Instance-Based Learning in Mixed Symbolic/Numeric Domains. In R.S. Michalski and G. Tecuci (eds.), Proceedings of the Second International Workshop on Multistrategy Learning (MSL-93), Harpers Ferry, W.VA.

Widmer, G. and Kubat, M. (1993).
Effective Learning in Dynamic Environments by Explicit Context Tracking. In Proceedings of the Sixth European Conference on Machine Learning (ECML-93), Springer, Berlin. [draft (.ps)]

Widmer, G. (1993).
Understanding and Learning Musical Expression. In Proceedings of the International Computer Music Conference (ICMC-93), Tokyo, Japan. San Francisco, CA: International Computer Music Association.

Widmer, G. and Kubat, M. (1992).
Learning Flexible Concepts from Streams of Examples: FLORA2. In B. Neumann (ed.), Proceedings of the Tenth European Conference on Artificial Intelligence (ECAI-92), Wiley, Chichester, UK. [draft (.ps)]

Widmer, G. (1992).
The Importance of Musicologically Meaningful Vocabularies for Learning. In Proceedings of the International Computer Music Conference (ICMC-92), San Jose. San Francisco, CA: International Computer Music Association.

Widmer, G. (1991).
Using Plausible Explanations to Bias Empirical Generalization in Weak Theory Domains. In Y. Kodratoff (ed.) Proceedings of the Fifth European Working Session on Learning (EWSL-91), Springer, Berlin.

Horn, W., Widmer, G. and Nagele, B. (1991).
Learning Specialized Disease Descriptions in a Rheumatological Expert System. In K.P. Adlassnig et al.(eds.), Medical Informatics Europe 1991 (MIE-91), Springer, Berlin, pp.322-326.

Widmer, G. (1991).
Learning by Plausible Reasoning and its Application to a Complex Musical Problem. In R.S. Michalski and G. Tecuci (eds.), Proceedings of the First International Workshop on Multistrategy Learning (MSL-91), Harpers Ferry, W.VA.

Widmer, G. (1990).
The Usefulness of Qualitative Theories of Musical Perception. In Proceedings of the International Computer Music Conference (ICMC-90), Glasgow, Scotland. San Francisco, CA: International Computer Music Association.

Widmer, G. (1990).
Learning a Complex Musical Task on the Basis of a Plausible Theory of Musical Perception. In Proceedings of the ECAI Workshop on Artificial Intelligence and Music, ECAI-90, Stockholm, Sweden.

Widmer, G. (1989).
A Tight Integration of Deductive and Inductive Learning. In A. Segre (ed.), Proceedings of the Sixth International Workshop on Machine Learning (ML-89). Morgan Kaufmann, Los Altos, CA.

Widmer, G. (1989).
An Incremental Version of Bergadano & Giordana's Integrated Learning Strategy. In K. Morik K.(ed.), Proceedings of the Fourth European Working Session on Learning (EWSL-89), Pitman, London.

Theses

Widmer, G. (1995).
Multistrategy Learning and Domain Modelling. "Habilitation thesis", Technical University of Vienna.

Widmer, G. (1989).
Ein neues Lernmodell zur engeren Integration analytischen und empirischen Lernens. Dissertation, Institut für Medizinische Kybernetik und Artificial Intelligence, Universität Wien.