Music Information Retrieval

@inproceedings{mediaeval19_tai,
 author = {Hsiao-Tzu Hung and Yu-Hua Chen and Maximilian Mayerl and Michael Vötter and Eva Zangerle and Yi-Hsuan Yang},
 booktitle = {Working Notes Proceedings of the MediaEval 2019 Workshop},
 month = {12},
 publisher = {ceur-ws.org},
 title = {MediaEval 2019 Emotion and Theme Recognition task: A VQ-VAE Based Approach},
 year = {2019}
}

@inproceedings{8516495,
title = {Latent Feature Combination for Multi-Context Music Recommendation},
author = {Martin Pichl and Eva Zangerle},
doi = {10.1109/CBMI.2018.8516495},
year = {2018},
date = {2018-09-01},
booktitle = {2018 International Conference on Content-Based Multimedia Indexing (CBMI)},
pages = {1-6},
abstract = {In recent years, music aficionados have increasingly been consuming music via public music streaming platforms. Due to the size of the collections provided, music recommender systems have become a vital component as these aim to provide recommendations that match the user's current context as, throughout the day, users listen to music in numerous different contexts and situations. In this paper, we propose a multi-context-aware track recommender system that jointly exploits information about the current situation and musical preferences of users. To jointly model users by their situational and musical preferences, we cluster users based on their situational features and similarly, cluster music tracks based on their content features. Our experiments show that by relying on Factorization Machines for the computation of recommendations, the proposed approach allows to successfully leverage interaction effects between listening histories, situational and track content information, substantially outperforming a set of baseline recommenders.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{zangerle_ismir18,
title = {Content-based User Models: Modeling the Many Faces of Musical Preference},
author = {Eva Zangerle and Martin Pichl},
editor = {Emilia Gomez and Xiao Hu and Eric Humphrey and Emmanouil Benetos},
year = {2018},
date = {2018-09-22},
booktitle = {Proceedings of the 19th International Society for Music Information Retrieval Conference 2018 (ISMIR 2018)},
pages = {709--716}
}

@inproceedings{smc18,
title = {#nowplaying-RS: A New Benchmark Dataset for Building Context-Aware Music Recommender Systems},
author = {Asmita Poddar and Eva Zangerle and Yi-Hsuan Yang },
doi = {10.5281/zenodo.1422565},
year = {2018},
booktitle = {Proceedings of the 15th Sound & Music Computing Conference},
address = {Limassol, Cyprus}
}

@inproceedings{Zangerle:2018:CMR:3209219.3209258,
 author = {Zangerle, Eva and Pichl, Martin and Schedl, Markus},
 title = {Culture-Aware Music Recommendation},
 booktitle = {Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization},
 series = {UMAP '18},
 year = {2018},
 isbn = {978-1-4503-5589-6},
 location = {Singapore, Singapore},
 pages = {357--358},
 numpages = {2},
 url = {http://doi.acm.org/10.1145/3209219.3209258},
 doi = {10.1145/3209219.3209258},
 acmid = {3209258},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {music information retrieval, recommender systems, user modeling},
} 

@inproceedings{Murauer:2018:DMG:3184558.3191822,
 author = {Murauer, Benjamin and Specht, G\"{u}nther},
 title = {Detecting Music Genre Using Extreme Gradient Boosting},
 booktitle = {Companion of the The Web Conference 2018 on The Web Conference 2018},
 series = {WWW '18},
 year = {2018},
 isbn = {978-1-4503-5640-4},
 location = {Lyon, France},
 pages = {1923--1927},
 numpages = {5},
 url = {https://doi.org/10.1145/3184558.3191822},
 doi = {10.1145/3184558.3191822},
 acmid = {3191822},
 publisher = {International World Wide Web Conferences Steering Committee},
 address = {Republic and Canton of Geneva, Switzerland},
 keywords = {gradient boosting, music classification, neural network},
} 

@inproceedings{DBLP:conf/ecir/zangerle17,
title = {ALF-200k: Towards Extensive Multimodal Analyses of Music Tracks and Playlists},
author = {Eva Zangerle and Michael Tschuggnall and Stefan Wurzinger and Günther Specht},
doi = {10.1007/978-3-319-76941-7_48},
isbn = {978-3-319-76941-7},
year = {2018},
date = {2018-01-01},
booktitle = {Advances in Information Retrieval - 39th European Conference on IR Research, ECIR 2018},
pages = {584--590},
publisher = {Springer},
address = {Cham},
abstract = {In recent years, approaches in music information retrieval have been based on multimodal analyses of music incorporating audio as well as lyrics features. Because most of those approaches are lacking reusable, high-quality datasets, in this work we propose ALF-200k, a publicly available, novel dataset including 176 audio and lyrics features of more than 200,000 tracks and their attribution to more than 11,000 user-created playlists. While the dataset is of general purpose and thus, may be used in experiments for diverse music information retrieval problems, we present a first multimodal study on playlist features and particularly analyze, which type of features are shared within specific playlists and thus, characterize it. We show that while acoustic features act as the major glue between tracks contained in a playlists, also lyrics features are a powerful means to attribute tracks to playlists.}
}

@inproceedings{milc2018,
title = {geMsearch: Personalized Explorative Music Search},
author = {Christian Esswein and Markus Schedl and Eva Zangerle},
url = {http://ceur-ws.org/Vol-2068/milc2.pdf},
year = {2018},
date = {2018-03-11},
booktitle = {Joint Proceedings of the ACM IUI 2018 Workshops co-located with the 23rd ACM Conference on Intelligent User Interfaces (ACM IUI 2018)},
publisher = {ceur-ws.org},
abstract = {Due to the rise of music streaming platforms, huge collections of music are now available to users on various devices. Within these collections, users aim to find and explore songs based on certain criteria reflecting their current and context-specific preferences. Currently, users are limited to either using search facilities or relying on recommender systems that suggest suitable tracks or artists. Using search facilities requires the user to have some idea about the targeted music and to formulate a query that accurately describes this music, whereas recommender systems are traditionally geared towards long-term shifts of user preferences in contrast to ad-hoc and interactive preference elicitation. To bridge this gap, we propose geMsearch, an approach for personalized, explorative music search based on graph embedding techniques. As the ecosystem of a music collection can be represented as a heterogeneous graph containing nodes describing e.g., tracks, artists, genres or users, we employ graph embedding techniques to learn low-dimensional vector representations for all nodes within the graph. This allows for efficient approximate querying of the collection and, more importantly, for employing visualization strategies that allow the user to explore the music collection in a 3D-space.}
}

@PHDTHESIS {pichl2018-diss,
    author = {Martin Pichl},
    title  = {{Multi-Context-Aware Recommender Systems: A Study on Music Recommendation}},
    school = {University of Innsbruck, Department of Computer Science},
    year   = {2018},
    month  = {feb}
}

@online{dha17_lyrics,
title = {Analyzing Coherent Characteristics in Music Playlists},
author = {Eva Zangerle and Michael Tschuggnall and Stefan Wurzinger and G\"{u}nther Specht},
year = {2017},
date = {2017-12-06},
organization = {4th Digital Humanities Austria Conference},
howpublished = {4th Digital Humanities Austria Conference},
url = {https://www.uibk.ac.at/congress/dha2017/bilder-und-dateien/poster_zangerle.pdf}
}

@inproceedings{sympmm,
title = {Mining Culture-Specific Music Listening Behavior from Social Media Data},
author = {Pichl, Martin and Zangerle, Eva and Specht, G\"unther and Markus Schedl},
doi = {10.1109/ISM.2017.35},
isbn = {978-1-5386-2937-6/17},
year = {2017},
booktitle = {IEEE International Symposium on Multimedia, ISM 2017, Taichung, Taiwan, December 11-13, 2017},
pages = {208--215},
publisher = {IEEE Computer Society}
}

@article{Murauer2017Genre,
  title={Hierarchical Multilabel Classification and Voting for Genre Classification},
  author={Murauer, Benjamin and Mayerl, Maximilian and Tschuggnall, Michael and Zangerle, Eva and Pichl, Martin and Specht, G{\"u}nther},
  booktitle={CEURS Working Notes Proceedings of the MediaEval 2017 Workshop},
  publisher={CEUR-WS.org},
  city={Dublin, Ireland},
  year={2017},
  url={http://ceur-ws.org/Vol-1984/Mediaeval_2017_paper_41.pdf},
}

@inproceedings{Pichl:2017:ICM:3078971.3078980,
 author = {Pichl, Martin and Zangerle, Eva and Specht, G\"{u}nther},
 title = {Improving Context-Aware Music Recommender Systems: Beyond the Pre-filtering Approach},
 booktitle = {Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval},
 series = {ICMR '17},
 year = {2017},
 isbn = {978-1-4503-4701-3},
 location = {Bucharest, Romania},
 pages = {201--208},
 numpages = {8},
 url = {http://doi.acm.org/10.1145/3078971.3078980},
 doi = {10.1145/3078971.3078980},
 acmid = {3078980},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {context, personalization, recommender systems, user modeling},
} 

@article{ijmdem17,
	author = {Martin Pichl and
	Eva Zangerle and
	G{\"{u}}nther Specht},
	title = {Understanding User-curated Playlists on Spotify: A Machine Learning Approach},
	journal = {International Journal of Multimedia Data Engineering and Management {IJMDEM}},
	volume = {8},
	number = {4}, 
	year = {2017}
}

@inProceedings{sympmm16,
title = {{Understanding Playlist Creation on Music Streaming Platforms}},
booktitle = {Proceedings of the IEEE Symposium on Multimedia (ISM)},
publisher = {IEEE},
year = {2016},
author = {Pichl, Martin and Zangerle, Eva and Specht, G\"unther},
pages = {475--480}
}

@inproceedings{DBLP:conf/ismir/ZangerlePHS16,
  author    = {Eva Zangerle and
               Martin Pichl and
               Benedikt Hupfauf and
               G{\"{u}}nther Specht},
  editor    = {Michael I. Mandel and
               Johanna Devaney and
               Douglas Turnbull and
               George Tzanetakis},
  title     = {Can Microblogs Predict Music Charts? An Analysis of the Relationship
               Between {\#}Nowplaying Tweets and Music Charts},
  booktitle = {Proceedings of the 17th International Society for Music Information
               Retrieval Conference, {ISMIR} 2016, New York City, United States,
               August 7-11, 2016},
  pages     = {365--371},
  year      = {2016},
  url       = {https://wp.nyu.edu/ismir2016/wp-content/uploads/sites/2294/2016/07/039_Paper.pdf},
  timestamp = {Thu, 08 Sep 2016 13:32:51 +0200},
  biburl    = {http://dblp.uni-trier.de/rec/bib/conf/ismir/ZangerlePHS16},
  bibsource = {dblp computer science bibliography, http://dblp.org},
  publisher = {ISMIR}
}

@inProceedings{somera15,
author = {Pichl, Martin and Zangerle, Eva and Specht, G\"{u}nther},
title = {Towards a Context-Aware Music Recommendation Approach: What is Hidden in the Playlist Name?},
booktitle = {15th IEEE International Conference on Data Mining Workshops (ICDM 2015)},
series = {ICDM 15},
year = {2015},
location = {Atlantic City},
pages = {1360--1365},
numpages = {6},
month = nov,
publisher = {IEEE},
address = {Piscataway, NJ, USA},
}

@inProceedings{sowemine15,
booktitle = {{Current Trends in Web Engineering, 15th International Conference, ICWE 2015 Workshops (Revised Selected Papers)}},
title = {{#nowplaying on #Spotify: Leveraging Spotify Information on Twitter for Artist Recommendations}},
publisher = {Springer},
year = {2015},
pages = {163--174},
author={Pichl, Martin and Zangerle, Eva and Specht, G\"unther}
}

@inproceedings{gvdb14,
author = {Pichl, Martin and Zangerle, Eva and Specht, G\"unther},
booktitle = {Proceedings of the 26nd Workshop Grundlagen von Datenbanken (GvDB 2014), Ritten, Italy},
publisher = {CEUR-WS.org},
title = {{Combining Spotify and Twitter Data for Generating a Recent and Public Dataset for Music Recommendation}},
year = {2014},
volume = {1313},
month = oct,
pages = {35--40},
howpublished = {online},
}

@InProceedings{ismm14,
title={#nowplaying Music Dataset: Extracting Listening Behavior from Twitter},
author={Zangerle, Eva and Pichl, Martin and Gassler, Wolfgang and Specht, G\"unther},
year={2014},
pages={21--26},
location = {Orlando, Florida, USA},
publisher = {ACM},
address = {New York, NY, USA},
month = jun,
booktitle = {Proceedings of the 1st ACM International Workshop on Internet-Scale Multimedia Management},
series = {ISMM '14},
}