Have a personal or library account? Click to login
Intelligent User Interfaces for Music Discovery Cover

Intelligent User Interfaces for Music Discovery

Transactions of the International Society for Music Information Retrieval
Volume 3 (2020): Issue 1
By: Peter Knees,  Markus Schedl and  Masataka Goto  
Open Access
|Oct 2020

References

  1. 1Andjelkovic, I., Parra, D., & O’Donovan, J. (2019). Moodplay: Interactive music recommendation based on artists’ mood similarity. International Journal of Human-Computer Studies, 121, 142–159. Advances in Computer-Human Interaction for Recommender Systems. DOI: 10.1016/j.ijhcs.2018.04.004
    Back to article
  2. 2Barrington, L., O’Malley, D., Turnbull, D., & Lanckriet, G. (2009). User-centered design of a social game to tag music. In Proceedings of the ACM SIGKDD Workshop on Human Computation (HCOMP 2009), pages 7–10. DOI: 10.1145/1600150.1600152
    Back to article
  3. 3Behrooz, M., Mennicken, S., Thom, J., Kumar, R., & Cramer, H. (2019). Augmenting music listening experiences on voice assistants. In Proceedings of the 20th International Society for Music Information Retrieval Conference, pages 303–310, Delft, The Netherlands. ISMIR.
    Back to article
  4. 4Bertin-Mahieux, T., Eck, D., Maillet, F., & Lamere, P. (2008). Autotagger: A model for predicting social tags from acoustic features on large music databases. Journal of New Music Research, 37(2), 115–135. DOI: 10.1080/09298210802479250
    Back to article
  5. 5Bischoff, K., Firan, C. S., Nejdl, W., & Paiu, R. (2008). Can all tags be used for search? In Proceedings of the 17th ACM Conference on Information and Knowledge Management, CIKM ’08, pages 193–202, New York, NY, USA. Association for Computing Machinery. DOI: 10.1145/1458082.1458112
    Back to article
  6. 6Bonnin, G., & Jannach, D. (2014). Automated generation of music playlists: Survey and experiments. ACM Computing Surveys, 47(2), 26:1–26:35. DOI: 10.1145/2652481
    Back to article
  7. 7Brown, B. A. T., Geelhoed, E., & Sellen, A. (2001). The use of conventional and new music media: Implications for future technologies. In Hirose, M., editor, Human-Computer Interaction INTERACT ’01: IFIP TC13 International Conference on Human-Computer Interaction, pages 67–75. IOS Press.
    Back to article
  8. 8Bull, M. (2006). Investigating the culture of mobile listening: From Walkman to iPod. In O’Hara, K. and Brown, B., editors, Consuming Music Together: Social and Collaborative Aspects of Music Consumption Technologies, pages 131–149. Springer Netherlands, Dordrecht. DOI: 10.1007/1-4020-4097-0_7
    Back to article
  9. 9Burke, R. (2000). Knowledge-based recommender systems. In Encyclopedia of Library and Information Systems, volume 69, pages 180–200. Marcel Dekker: New York, NY, USA.
    Back to article
  10. 10Byrd, D., & Fingerhut, M. (2002). The history of ISMIR – a short happy tale. D-Lib Magazine, 8(11).
    Back to article
  11. 11Casey, M., Veltkamp, R., Goto, M., Leman, M., Rhodes, C., & Slaney, M. (2008). Content-based music information retrieval: Current directions and future challenges. Proceedings of the IEEE, 96(4), 668–696. DOI: 10.1109/JPROC.2008.916370
    Back to article
  12. 12Celma, O. (2010). Music Recommendation and Discovery – The Long Tail, Long Fail, and Long Play in the Digital Music Space. Springer. DOI: 10.1007/978-3-642-13287-2
    Back to article
  13. 13Chen, L., & Pu, P. (2012). Critiquing-based recommenders: Survey and emerging trends. User Modeling and User-Adapted Interaction, 22(1), 125–150. DOI: 10.1007/s11257-011-9108-6
    Back to article
  14. 14Cunningham, S. J. (2019). Interacting with personal music collections. In Taylor, N. G., Christian-Lamb, C., Martin, M. H., & Nardi, B., editors, Information in Contemporary Society, pages 526–536, Cham. Springer International Publishing. DOI: 10.1007/978-3-030-15742-5_50
    Back to article
  15. 15Cunningham, S. J., Bainbridge, D., & Bainbridge, A. (2017). Exploring personal music collection behavior. In Choemprayong, S., Crestani, F., & Cunningham, S. J., editors, Digital Libraries: Data, Information, and Knowledge for Digital Lives, pages 295–306, Cham. Springer International Publishing. DOI: 10.1007/978-3-319-70232-2_25
    Back to article
  16. 16Davies, M. E. P., Hamel, P., Yoshii, K., & Goto, M. (2014). AutoMashUpper: Automatic creation of multi-song music mashups. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 22(12), 1726–1737. DOI: 10.1109/TASLP.2014.2347135
    Back to article
  17. 17Deng, S., Wang, D., Li, X., & Xu, G. (2015). Exploring user emotion in microblogs for music recommendation. Expert Systems with Applications, 42(23), 9284–9293. DOI: 10.1016/j.eswa.2015.08.029
    Back to article
  18. 18Dinculescu, M., Engel, J., & Roberts, A. (2019). MidiMe: Personalizing a MusicVAE model with user data. In NeurIPS Workshop on Machine Learning for Creativity and Design.
    Back to article
  19. 19Dittenbach, M., Merkl, D., & Rauber, A. (2001). Hierarchical clustering of document archives with the growing hierarchical self-organizing map. In Proceedings of the International Conference on Artificial Neural Networks (ICANN 2001). DOI: 10.1007/3-540-44668-0_70
    Back to article
  20. 20Downie, J. S. (2004). The scientific evaluation of music information retrieval systems: Foundations and future. Computer Music Journal, 28, 12–23. DOI: 10.1162/014892604323112211
    Back to article
  21. 21Dredge, S. (2018). Everybody’s talkin’: Smart speakers & their impact on music consumption. Technical report, Music Ally report to BPI and ERA.
    Back to article
  22. 22Eck, D., Lamere, P., Bertin-Mahieux, T., & Green, S. (2008). Automatic generation of social tags for music recommendation. In Advances in Neural Information Processing Systems 20 (NIPS 2007).
    Back to article
  23. 23Fernández-Tobías, I., Braunhofer, M., Elahi, M., Ricci, F., & Cantador, I. (2016). Alleviating the new user problem in collaborative filtering by exploiting personality information. User Modeling and User-Adapted Interaction, 26(2–3), 221–255. DOI: 10.1007/s11257-016-9172-z
    Back to article
  24. 24Ferwerda, B., Tkalcic, M., & Schedl, M. (2017). Personality traits and music genres: What do people prefer to listen to? In Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization (UMAP 2017), pages 285–288. DOI: 10.1145/3079628.3079693
    Back to article
  25. 25Ferwerda, B., Yang, E., Schedl, M., & Tkalcic, M. (2015). Personality traits predict music taxonomy preferences. In Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems, CHI EA ’15, pages 2241–2246, New York, NY, USA. Association for Computing Machinery. DOI: 10.1145/2702613.2732754
    Back to article
  26. 26Flexer, A., Schnitzer, D., Gasser, M., & Widmer, G. (2008). Playlist generation using start and end songs. In Proceedings of the 9th International Conference on Music Information Retrieval (ISMIR 2008), pages 173–178.
    Back to article
  27. 27Fujihara, H., Goto, M., Kitahara, T., & Okuno, H. G. (2010). A modeling of singing voice robust to accompaniment sounds and its application to singer identification and vocal-timbre-similaritybased music information retrieval. IEEE Transactions on Audio, Speech, and Language Processing, 18(3), 638–648. DOI: 10.1109/TASL.2010.2041386
    Back to article
  28. 28Gasser, M., & Flexer, A. (2009). FM4 Soundpark: Audio-based music recommendation in everyday use. In Proceedings of the 6th Sound and Music Computing Conference (SMC 2009).
    Back to article
  29. 29Goto, M. (2007). Active music listening interfaces based on signal processing. In Proceedings of the 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pages 1441–1444. DOI: 10.1109/ICASSP.2007.367351
    Back to article
  30. 30Goto, M. (2012). Grand challenges in music information research. In Muller, M., Goto, M., & Schedl, M., editors, Dagstuhl Follow-Ups: Multimodal Music Processing, pages 217–225. Dagstuhl Publishing.
    Back to article
  31. 31Goto, M., & Dannenberg, R. B. (2019). Music interfaces based on automatic music signal analysis: New ways to create and listen to music. IEEE Signal Processing Magazine, 36(1), 74–81. DOI: 10.1109/MSP.2018.2874360
    Back to article
  32. 32Goto, M., & Goto, T. (2009). Musicream: Integrated music-listening interface for active, flexible, and unexpected encounters with musical pieces. IPSJ (Information Processing Society of Japan) Journal, 50(12), 2923–2936. DOI: 10.2197/ipsjjip.17.292
    Back to article
  33. 33Govaerts, S., & Duval, E. (2009). A web-based approach to determine the origin of an artist. In Proceedings of the 10th International Society for Music Information Retrieval Conference (ISMIR 2009).
    Back to article
  34. 34Hagen, A. N. (2015). The playlist experience: Personal playlists in music streaming services. Popular Music and Society, 38(5), 625–645. DOI: 10.1080/03007766.2015.1021174
    Back to article
  35. 35Hamasaki, M., Goto, M., & Nakano, T. (2015). Songrium: Browsing and listening environment for music content creation community. In Proceedings of the 12th Sound and Music Computing Conference (SMC 2015), pages 23–30.
    Back to article
  36. 36Hariri, N., Mobasher, B., & Burke, R. (2012). Context-aware music recommendation based on latent topic sequential patterns. In Proceedings of the 6th ACM Conference on Recommender Systems (RecSys 2012), pages 131–138. DOI: 10.1145/2365952.2365979
    Back to article
  37. 37Hauger, D., & Schedl, M. (2012). Exploring geospatial music listening patterns in microblog data. In Proceedings of the 10th International Workshop on Adaptive Multimedia Retrieval (AMR 2012).
    Back to article
  38. 38Herrera, P. (2018). MIRages: An account of music audio extractors, semantic description and contextawareness, in the three ages of MIR. PhD thesis, Universitat Pompeu Fabra, Barcelona, Spain.
    Back to article
  39. 39Hong, J.-W., & Curran, N. M. (2019). Artificial intelligence, artists, and art: Attitudes toward artwork produced by humans vs artificial intelligence. ACM Transactions on Multimedia Computing, Communications and Applications, 15(2s). DOI: 10.1145/3326337
    Back to article
  40. 40Hu, Y., Koren, Y., & Volinsky, C. (2008). Collaborative filtering for implicit feedback datasets. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM), pages 263–272. DOI: 10.1109/ICDM.2008.22
    Back to article
  41. 41Huang, C.-Z. A., Vaswani, A., Uszkoreit, J., Shazeer, N., Simon, I., Hawthorne, C., Dai, A., Hoffman, M., Dinculescu, M., & Eck, D. (2019). Music transformer: Generating music with long-term structure. In Proceedings of the 7th International Conference on Learning Representations (ICLR 2019).
    Back to article
  42. 42Huber, S., Schedl, M., & Knees, P. (2012). nepDroid: An intelligent mobile music player. In Proceedings of the ACM International Conference on Multimedia Retrieval (ACM ICMR 2012). DOI: 10.1145/2324796.2324862
    Back to article
  43. 43Jannach, D., Lerche, L., & Zanker, M. (2018). Recommending based on implicit feedback. In Brusilovsky, P. and He, D., editors, Social Information Access: Systems and Technologies, pages 510–569. Springer International Publishing: Cham. DOI: 10.1007/978-3-319-90092-6_14
    Back to article
  44. 44Jin, Y., Cai, W., Chen, L., Htun, N. N., & Verbert, K. (2019). MusicBot: Evaluating critiquing-based music recommenders with conversational interaction. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM ’19, pages 951–960, New York, NY, USA. Association for Computing Machinery. DOI: 10.1145/3357384.3357923
    Back to article
  45. 45Jin, Y., Tintarev, N., & Verbert, K. (2018). Effects of personal characteristics on music recommender systems with different levels of controllability. In Proceedings of the 12th ACM Conference on Recommender Systems (RecSys 2018), pages 13–21. DOI: 10.1145/3240323.3240358
    Back to article
  46. 46John, O. P., Donahue, E. M., & Kentle, R. L. (1991). The Big Five Inventory — Versions 4a and 54. University of California, Berkeley, Institute of Personality and Social Research. DOI: 10.1037/t07550-000
    Back to article
  47. 47Julià, C. F., & Jordà, S. (2009). SongExplorer: A tabletop application for exploring large collections of songs. In Proceedings of the 10th International Society for Music Information Retrieval Conference (ISMIR 2009).
    Back to article
  48. 48Kageyama, T., Mochizuki, K., & Takashima, Y. (1993). Melody retrieval with humming. In Proceedings of the 1993 International Computer Music Conference (ICMC 1993), pages 349–351.
    Back to article
  49. 49Kamehkhosh, I., Bonnin, G., & Jannach, D. (2020). Effects of recommendations on the playlist creation behavior of users. User Modeling and User-Adapted Interaction, 30, 285–322. DOI: 10.1007/s11257-019-09237-4
    Back to article
  50. 50Kim, J. H., Tomasik, B., & Turnbull, D. (2009). Using artist similarity to propagate semantic information. In Proceedings of the 10th International Society for Music Information Retrieval Conference (ISMIR 2009).
    Back to article
  51. 51Kim, Y. E., Schmidt, E. M., & Emelle, L. (2008). MoodSwings: A collaborative game for music mood label collection. In Proceedings of the 9th International Conference on Music Information Retrieval (ISMIR 2008), pages 231–236.
    Back to article
  52. 52Knees, P., & Schedl, M. (2013). A survey of music similarity and recommendation from music context data. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP), 10(1). DOI: 10.1145/2542205.2542206
    Back to article
  53. 53Knees, P., Schedl, M., Ferwerda, B., & Laplante, A. (2019). User awareness in music recommender systems. In Augstein, M., Herder, E., & Wörndl, W., editors, Personalized Human-Computer Interaction, pages 223–252. DeGruyter: Berlin, Boston. DOI: 10.1515/9783110552485-009
    Back to article
  54. 54Knees, P., Schedl, M., Pohle, T., & Widmer, G. (2006). An innovative three-dimensional user interface for exploring music collections enriched with meta-information from the web. In Proceedings of the 14th ACM International Conference on Multimedia (ACM Multimedia 2006). DOI: 10.1145/1180639.1180652
    Back to article
  55. 55Lamere, P. (2008). Social tagging and music information retrieval. Journal of New Music Research, 37(2), 101–114. DOI: 10.1080/09298210802479284
    Back to article
  56. 56Lamere, P., & Eck, D. (2007). Using 3D visualizations to explore and discover music. In Proceedings of the 8th International Conference on Music Information Retrieval (ISMIR 2007).
    Back to article
  57. 57Law, E. L. M., von Ahn, L., Dannenberg, R. B., & Crawford, M. (2007). TagATune: A game for music and sound annotation. In Proceedings of the 8th International Conference on Music Information Retrieval (ISMIR 2007), pages 361–364.
    Back to article
  58. 58Lee, J. H., Kim, Y.-S., & Hubbles, C. (2016). A look at the cloud from both sides now: An analysis of cloud music service usage. In Proceedings of the 17th International Society for Music Information Retrieval Conference, pages 299–305, New York City, United States. ISMIR.
    Back to article
  59. 59Lehtiniemi, A., & Holm, J. (2013). Designing for music discovery: Evaluation and comparison of five music player prototypes. Journal of New Music Research, 42(3), 283–302. DOI: 10.1080/09298215.2013.796997
    Back to article
  60. 60Leitich, S., & Topf, M. (2007). Globe of Music – music library visualization using GeoSOM. In Proceedings of the 8th International Conference on Music Information Retrieval (ISMIR 2007).
    Back to article
  61. 61Liang, Y., & Willemsen, M. C. (2019). Personalized recommendations for music genre exploration. In Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization, UMAP ’19, pages 276–284, New York, NY, USA. Association for Computing Machinery. DOI: 10.1145/3320435.3320455
    Back to article
  62. 62Lonsdale, A. J., & North, A. C. (2011). Why do we listen to music? a uses and gratifications analysis. British Journal of Psychology, 102(1), 108–134. DOI: 10.1348/000712610X506831
    Back to article
  63. 63Louie, R., Coenen, A., Huang, C. Z., Terry, M., & Cai, C. J. (2020). Novice-AI music co-creation via AI-steering tools for deep generative models. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, New York, NY, USA. Association for Computing Machinery. DOI: 10.1145/3313831.3376739
    Back to article
  64. 64Lu, F., & Tintarev, N. (2018). A diversity adjusting strategy with personality for music recommendation. In Proceedings of the 5th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems, co-located with ACM Conference on Recommender Systems (RecSys 2018).
    Back to article
  65. 65Lübbers, D., & Jarke, M. (2009). Adaptive multimodal exploration of music collections. In Proceedings of the 10th International Society for Music Information Retrieval Conference (ISMIR 2009).
    Back to article
  66. 66Mai, J.-E. (2011). Folksonomies and the new order: Authority in the digital disorder. Knowledge Organization, 38(2), 114–122. DOI: 10.5771/0943-7444-2011-2-114
    Back to article
  67. 67Mandel, M. I., & Ellis, D. P. (2008). A web-based game for collecting music metadata. Journal of New Music Research, 37(2), 151–165. DOI: 10.1080/09298210802479300
    Back to article
  68. 68Mandel, M. I., Pascanu, R., Eck, D., Bengio, Y., Aiello, L. M., Schifanella, R., & Menczer, F. (2011). Contextual tag inference. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP), 7S(1), 32:1–32:18. DOI: 10.1145/2037676.2037689
    Back to article
  69. 69McFee, B., & Lanckriet, G. (2011). The natural language of playlists. In Proceedings of the 12th International Society for Music Information Retrieval Conference (ISMIR 2011).
    Back to article
  70. 70Millecamp, M., Htun, N. N., Jin, Y., & Verbert, K. (2018). Controlling Spotify recommendations: Effects of personal characteristics on music recommender user interfaces. In Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization, UMAP ’18, pages 101–109, New York, NY, USA. Association for Computing Machinery. DOI: 10.1145/3209219.3209223
    Back to article
  71. 71Mörchen, F., Ultsch, A., Nöcker, M., & Stamm, C. (2005). Databionic visualization of music collections according to perceptual distance. In Proceedings of the 6th International Conference on Music Information Retrieval (ISMIR 2005).
    Back to article
  72. 72Müllensiefen, D., Gingras, B., Musil, J., & Stewart, L. (2014). The musicality of non-musicians: An index for assessing musical sophistication in the general population. PLOS ONE, 9(2), 1–23. DOI: 10.1371/journal.pone.0089642
    Back to article
  73. 73Neumayer, R., Dittenbach, M., & Rauber, A. (2005). PlaySOM and PocketSOMPlayer, alternative interfaces to large music collections. In Proceedings of the 6th International Conference on Music Information Retrieval (ISMIR 2005).
    Back to article
  74. 74North, A. C., Hargreaves, D. J., & Hargreaves, J. J. (2004). Uses of music in everyday life. Music Perception, 22(1), 41–77. DOI: 10.1525/mp.2004.22.1.41
    Back to article
  75. 75Oramas, S., Ostuni, V. C., Noia, T. D., Serra, X., & Sciascio, E. D. (2016). Sound and music recommendation with knowledge graphs. ACM Transactions on Intelligent Systems and Technology, 8(2). DOI: 10.1145/2926718
    Back to article
  76. 76Pampalk, E., Dixon, S., & Widmer, G. (2004). Exploring music collections by browsing different views. Computer Music Journal, 28(2), 49–62. DOI: 10.1162/014892604323112248
    Back to article
  77. 77Pampalk, E., & Goto, M. (2006). MusicRainbow: A new user interface to discover artists using audiobased similarity and web-based labeling. In Proceedings of the 7th International Conference on Music Information Retrieval (ISMIR 2006).
    Back to article
  78. 78Pampalk, E., & Goto, M. (2007). MusicSun: A new approach to artist recommendation. In Proceedings of the 8th International Conference on Music Information Retrieval (ISMIR 2007).
    Back to article
  79. 79Pampalk, E., Rauber, A., & Merkl, D. (2002). Content-based organization and visualization of music archives. In Proceedings of the 10th ACM International Conference on Multimedia (MM 2002), pages 570–579, Juan les Pins, France. DOI: 10.1145/641007.641121
    Back to article
  80. 80Pohle, T., Knees, P., Schedl, M., Pampalk, E., & Widmer, G. (2007). “Reinventing the Wheel”: A novel approach to music player interfaces. IEEE Transactions on Multimedia, 9(3), 567–575. DOI: 10.1109/TMM.2006.887991
    Back to article
  81. 81Prockup, M., Ehmann, A. F., Gouyon, F., Schmidt, E., Celma, Ò., & Kim, Y. E. (2015). Modeling genre with the Music Genome Project: Comparing human-labeled attributes and audio features. In Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR), Málaga, Spain.
    Back to article
  82. 82Raimond, Y. (2008). A Distributed Music Information System. PhD thesis, Queen Mary University of London, UK.
    Back to article
  83. 83Raimond, Y., Abdallah, S., Sandler, M., & Giasson, F. (2007). The Music Ontology. In Proceedings of the 8th International Conference on Music Information Retrieval (ISMIR 2007), Vienna, Austria.
    Back to article
  84. 84Rentfrow, P. J., & Gosling, S. D. (2003). The do re mi’s of everyday life: The structure and personality correlates of music preferences. Journal of Personality and Social Psychology, 84(6), 1236–1256. DOI: 10.1037/0022-3514.84.6.1236
    Back to article
  85. 85Roberts, A., Engel, J., Oore, S., & Eck, D. (2018a). Learning latent representations of music to generate interactive musical palettes. In Proceedings of the 2018 ACM Workshop on Intelligent Music Interfaces for Listening and Creation (MILC 2018).
    Back to article
  86. 86Roberts, A., Engel, J., Raffel, C., Hawthorne, C., & Eck, D. (2018b). A hierarchical latent vector model for learning long-term structure in music. In Proceedings of the 35th International Conference on Machine Learning (ICML 2018), pages 4364–4373.
    Back to article
  87. 87Sasaki, S., Yoshii, K., Nakano, T., Goto, M., & Morisihima, S. (2014). LyricsRadar: A lyrics retrieval system based on latent topics of lyrics. In Proceedings of the 15th International Society for Music Information Retrieval Conference (ISMIR 2014), pages 585–590.
    Back to article
  88. 88Schedl, M., Gómez, E., Trent, E., Tkalčič, M., Eghbal-Zadeh, H., & Martorell, A. (2018). On the interrelation between listener characteristics and the perception of emotions in classical orchestra music. IEEE Transactions on Affective Computing, 9, 507–525. DOI: 10.1109/TAFFC.2017.2663421
    Back to article
  89. 89Schedl, M., Höglinger, C., & Knees, P. (2011a). Large-scale music exploration in hierarchically organized landscapes using prototypicality information. In Proceedings of the ACM International Conference on Multimedia Retrieval (ACM ICMR 2011). DOI: 10.1145/1991996.1992004
    Back to article
  90. 90Schedl, M., Knees, P., McFee, B., Bogdanov, D., & Kaminskas, M. (2015). Music recommender systems. In Ricci, F., Rokach, L., Shapira, B., & Kantor, P. B., editors, Recommender Systems Handbook, pages 453–492. Springer, 2nd edition. DOI: 10.1007/978-1-4899-7637-6_13
    Back to article
  91. 91Schedl, M., Widmer, G., Knees, P., & Pohle, T. (2011b). A music information system automatically generated via web content mining techniques. Information Processing & Management, 47, 426–439. DOI: 10.1016/j.ipm.2010.09.002
    Back to article
  92. 92Schnitzer, D., Pohle, T., Knees, P., & 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), pages 103–109. DOI: 10.1145/1329469.1329483
    Back to article
  93. 93Slaney, M. (2011). Web-scale multimedia analysis: Does content matter? IEEE MultiMedia, 18(2), 12–15. DOI: 10.1109/MMUL.2011.34
    Back to article
  94. 94Slaney, M., & White, W. (2007). Similarity based on rating data. In Proceedings of the 8th International Conference on Music Information Retrieval (ISMIR 2007), pages 479–484.
    Back to article
  95. 95Sordo, M. (2012). Semantic Annotation of Music Collections: A Computational Approach. PhD thesis, Universitat Pompeu Fabra, Barcelona, Spain.
    Back to article
  96. 96Stewart, R., Levy, M., & Sandler, M. (2008). 3D interactive environment for music collection navigation. In Proceedings of the 11th International Conference on Digital Audio Effects (DAFx-08).
    Back to article
  97. 97Stober, S., & Nürnberger, A. (2010). MusicGalaxy — an adaptive user-interface for exploratory music retrieval. In Proceedings of the 7th Sound and Music Computing Conference (SMC 2010), pages 23–30.
    Back to article
  98. 98Sturm, B. L. T., Iglesias, M., Ben-Tal, O., Miron, M., & Gómez, E. (2019). Artificial intelligence and music: Open questions of copyright law and engineering praxis. Arts, 8(3). DOI: 10.3390/arts8030115
    Back to article
  99. 99Surowiecki, J. (2004). The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations. Doubleday.
    Back to article
  100. 100Takahashi, T., Fukayama, S., & Goto, M. (2018). Instrudive: A music visualization system based on automatically recognized instrumentation. In Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR 2018), pages 561–568.
    Back to article
  101. 101Tsukuda, K., Ishida, K., & Goto, M. (2017). Lyric Jumper: A lyrics-based music exploratory web service by modeling lyrics generative process. In Proceedings of the 18th International Society for Music Information Retrieval Conference (ISMIR 2017), pages 544–551.
    Back to article
  102. 102Turnbull, D., Barrington, L., Torres, D., & Lanckriet, G. (2007a). Towards musical query-by-semanticdescription using the CAL500 data set. In Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (ACM SIGIR 2007). DOI: 10.1145/1277741.1277817
    Back to article
  103. 103Turnbull, D., Liu, R., Barrington, L., & Lanckriet, G. (2007b). A game-based approach for collecting semantic annotations of music. In Proceedings of the 8th International Conference on Music Information Retrieval (ISMIR 2007), Vienna, Austria.
    Back to article
  104. 104Tzanetakis, G., Essl, G., & Cook, P. (2001). Automatic musical genre classification of audio signals. In Proceedings of the 2nd International Symposium on Music Information Retrieval (ISMIR 2001), pages 205–210.
    Back to article
  105. 105Ultsch, A., & Siemon, H. P. (1990). Kohonen’s selforganizing feature maps for exploratory data analysis. In Proceedings of the International Neural Network Conference (INNC 1990), pages 305–308.
    Back to article
  106. 106Vad, B., Boland, D., Williamson, J., Murray-Smith, R., & Steffensen, P. B. (2015). Design and evaluation of a probabilistic music projection interface. In Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR 2015), pages 134–140.
    Back to article
  107. 107van der Maaten, L., & Hinton, G. (2008). Visualizing high-dimensional data using t-SNE. Journal of Machine Learning Research, 9, 2579–2605.
    Back to article
  108. 108van Gulik, R., & Vignoli, F. (2005). Visual playlist generation on the artist map. In Proceedings of the 6th International Conference on Music Information Retrieval (ISMIR 2005), pages 520–523.
    Back to article
  109. 109Vembu, S., & Baumann, S. (2004). A self-organizing map based knowledge discovery for music recommendation systems. In Proceedings of the 2nd International Symposium on Computer Music Modeling and Retrieval (CMMR 2004). DOI: 10.1007/978-3-540-31807-1_9
    Back to article
  110. 110Wang, X., Rosenblum, D., & Wang, Y. (2012). Context-aware mobile music recommendation for daily activities. In Proceedings of the 20th ACM International Conference on Multimedia (ACM Multimedia 2012), pages 99–108. DOI: 10.1145/2393347.2393368
    Back to article
  111. 111Whitman, B., & Ellis, D. P. W. (2004). Automatic record reviews. In Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR 2004), pages 470–477.
    Back to article
  112. 112Wold, E., Blum, T., Keislar, D., & Wheaton, J. (1996). Content-based classification, search, and retrieval of audio. IEEE MultiMedia, 3(3), 27–36. DOI: 10.1109/93.556537
    Back to article
  113. 113Wu, Y., & Takatsuka, M. (2006). Spherical selforganizing map using efficient indexed geodesic data structure. Neural Networks, 19(6–7), 900–910. DOI: 10.1016/j.neunet.2006.05.021
    Back to article
  114. 114Yakura, H., Nakano, T., & Goto, M. (2018). Focus-MusicRecommender: A system for recommending music to listen to while working. In Proceedings of the 23rd International Conference on Intelligent User Interfaces (ACM IUI 2018), pages 7–17. DOI: 10.1145/3172944.3172981
    Back to article
  115. 115Yoshii, K., Goto, M., Komatani, K., Ogata, T., & Okuno, H. G. (2007). Drumix: An audio player with functions of realtime drum-part rearrangement for active music listening. IPSJ (Information Processing Society of Japan) Journal, 48(3), 1229–1239. DOI: 10.2197/ipsjdc.3.134
    Back to article
  116. 116Zhou, C., Jin, Y., Zhang, K., Yuan, J., Li, S., & Wang, X. (2018). MusicRoBot: Towards conversational context-aware music recommender system. In Pei, J., Manolopoulos, Y., Sadiq, S., & Li, J., editors, Database Systems for Advanced Applications, pages 817–820, Cham. Springer International Publishing. DOI: 10.1007/978-3-319-91458-9_55
    Back to article
DOI: https://doi.org/10.5334/tismir.60 | Journal eISSN: 2514-3298
Journal RSS Feed
Language: English
Submitted on: Mar 19, 2020
Accepted on: Sep 2, 2020
Published on: Oct 16, 2020
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
music access,
music browsing,
user interfaces,
content-based MIR,
community metadata,
recommender systems

© 2020 Peter Knees, Markus Schedl, Masataka Goto, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.

Previous articleVolume 3 (2020): Issue 1Next article