References
- [1] C. Apté, F. Damerau, and S. M. Weiss. Toward language independent automated learning of text categorization models. In Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 23–30, Dublin, Ireland, 1994. Springer-Verlag. ⇒171
- [2] J. Atherton and B. Kaneshiro. I said it first: Topological analysis of lyrical influence networks. In ISMIR, pages 654–660, 2016. ⇒162
- [3] T. Bertin-Mahieux, D. P. W. Ellis, B. Whitman,and P. Lamere. The million song dataset. In A. Klapuri and C. Leider, editors, ISMIR, pages 591–596. University of Miami, 2011. ⇒159, 160
- [4] M. Besson, F. Faita, I. Peretz, A.-M. Bonnel, and J. Requin. Singing in the brain: Independence of lyrics and tunes. Psychological Science, 9(6):494–498, 1998. ⇒160, 169
- [5] C. M. Bishop. Pattern recognition and machine learning. Springer, 2006. ⇒174
- [6] M. J. T. Carneiro. Towards the discovery of temporal patterns in music listening using Last.fm profiles. Master’s thesis, Faculdade de Engenharia da Universidade do Porto, 2011. ⇒170
- [7] O. Chapelle, B. Schölkopf, and A. Zien. Semi-Supervised Learning. The MIT Press, 2006. ⇒178
- [8] K. Choi, Gy. Fazekas, M. Sandler, and K. Cho. Convolutional recurrent neural networks for music classification. In ICASSP, pages 2392–2396. IEEE, 2017. ⇒161
- [9] K. Choi, J. H. Lee, X. Hu, and J. S. Downie. Music subject classification based on lyrics and user interpretations. In Proceedings of the 79th ASIS&T Annual Meeting: Creating Knowledge, Enhancing Lives through Information & Technology. American Society for Information Science, 2016. ⇒161
- [10] H. Corona and M. P. O’Mahony. An exploration of mood classification in the million songs dataset. In 12th Sound and Music Computing Conference,Ireland, 2015. Music Technology Research Group, Department of Computer Science, Maynooth University. ⇒161
- [11] D. R. Cox. The regression analysis of binary sequences. Journal of the Royal Statistical Society. Series B (Methodological), 2(2):215–242, 1958. ⇒174
- [12] S. Dieleman, P. Brakel, and B. Schrauwen. Audio-based music classification with a pretrained convolutional network. In ISMIR, pages 669–674, 2011. ⇒161
- [13] S. Dieleman and B. Schrauwen. Multiscale approaches to music audio feature learning. In ISMIR, pages 116–121, 2013. ⇒161
- [14] S. Dieleman and B. Schrauwen. End-to-end learning for music audio. In ICASSP, pages 6964–6968. IEEE, 2014. ⇒161
- [15] D. P. W. Ellis. Extracting information from music audio. Communications of the ACM, 49(8):32–37, 2006. ⇒160
- [16] R. J. Ellis, Z. Xing, J. Fang, and Y. Wang. Quantifying lexical novelty in song lyrics. In ISMIR, pages 694–700, 2015. ⇒162
- [17] M. Fell and C. Sporleder. Lyrics-based analysis and classification of music. In J. Hajic and J. Tsujii, editors, COLING, pages 620–631. ACL, 2014. ⇒ 159, 161, 170, 172, 174
- [18] J. Fürnkranz. A study using n-gram features for text categorization, 1998. ⇒171
- [19] W. H. Gomaa and A. A. Fahmy. A survey of text similarity approaches. International Journal of Computer Applications, 68(13):13–18, April 2013. ⇒172
- [20] S. Gupta. Music data analysis: A state-of-the-art survey. arXiv preprint arXiv:1411.5014, 2014. ⇒160
- [21] P. Hamel and D. Eck. Learning features from music audio with deep belief networks. In ISMIR, volume 10, pages 339–344, 2010. ⇒161
- [22] H. Hirjee and D. G. Brown. Using automated rhyme detection to characterize rhyming style in rap music. Empirical Musicology Review, 5(4), 2010. ⇒172, 178
- [23] D. Jurafsky and J. H. Martin. Speech and language processing. 2017. 3rd edition draft. ⇒177
- [24] A. Kiss. Classification of hungarian folk music from Transylvania with convolutional neural networks. Master’s thesis, Faculty of Mathematics and Computer Science,Babeş–Bolyai University, Romania, 2018. ⇒161
- [25] P. Knees and M. Schedl. Music Similarity and Retrieval. Springer, Berlin–Heidelberg, 2016. ⇒160
- [26] P. Knees, M. Schedl, and G. Widmer. Multiple lyrics alignment: Automatic retrieval of song lyrics. In ISMIR, pages 564–569, 2005. ⇒160
- [27] D. E. Knuth. The Art of Computer Programming, Vol. 3: Sorting and Searching. Addison-Wesley, Reading, MA, 1973. ⇒172
- [28] Q. Le and T. Mikolov. Distributed representations of sentences and documents. In Proceedings of The 31st International Conference on Machine Learning, pages 1188–1196, 2014. ⇒178
- [29] V. I. Levenshtein. Binary codes capable of correcting deletions, insertions, and reversals. Soviet physics doklady, 10(8):707–710, 1966. ⇒172
- [30] T. L. H. Li, A. B. Chan,and A. Chun. Automatic musical pattern feature extraction using convolutional neural network. In Proc. Int. Conf. Data Mining and Applications, 2010. ⇒161
- [31] D. Liang,H.Gu, and B. O’Connor. Music genre classification with the million song dataset. Technical report, Machine Learning Department, CMU, 2011. ⇒161, 170
- [32] J. P. G. Mahedero, A. Martinez, P. Cano, M. Koppenberger, and F. Gouyon. Natural language processing of lyrics. In ACM Multimedia, pages 475–478. ACM, 2005. ⇒170
- [33] R. Malheiro, R. Panda, P. Gomes, and R. Paiva. Classification and regression of music lyrics: Emotionally-significant features. In 8th International Conference on Knowledge Discovery and Information Retrieval, Porto, Portugal, 2016. ⇒161
- [34] C. D. Manning, P. Raghavan,and H. Schütze. Introduction to information retrieval. Cambridge University Press, 2008. ⇒174
- [35] M. Mauch, R. M. MacCallum, M. Levy, and A. M. Leroi. The evolution of popular music: USA 1960–2010. Royal Society Open Science, 2(5), 2015. ⇒163
- [36] R. Mayer, R. Neumayer, and A. Rauber. Rhyme and style features for musical genre classification by song lyrics. In J. P. Bello, E. Chew, and D. Turnbull, editors, ISMIR, pages 337–342, 2008. ⇒161, 170, 173, 174
- [37] R. Mayer and A. Rauber. Music genre classification by ensembles of audio and lyrics features. In A. Klapuri and C. Leider, editors, ISMIR, pages 675–680. University of Miami, 2011. ⇒159
- [38] C. McKay and I. Fujinaga. Musical genre classification: Is it worth pursuing and how can it be improved? In ISMIR, pages 101–106, 2006. ⇒161, 169, 170
- [39] J.-B. Michel, Y. K. Shen, A. P. Aiden, A. Veres, M. K. Gray, J. P. Pickett, D. Hoiberg,D.Clancy, P. Norvig,J.Orwant, S. Pinker, M. A. Nowak,and E. Lieberman Aiden. Quantitative analysis of culture using millions of digitized books. Science, 331:176–182, 2011. ⇒178
- [40] T. Mikolov, K. Chen, G. Corrado,and J. Dean. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781, 2013. ⇒178
- [41] T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado,and J. Dean. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems, pages 3111–3119, 2013. ⇒178
- [42] L. Németh. Automatic non-standard hyphenation in OpenOffice.org. TUGboat, 27(1):32–37, 2006. ⇒172
- [43] F. Pachet and D. Cazaly. A taxonomy of musical genres. In J.-J. Mariani and D. Harman, editors, RIAO, pages 1238–1245. CID, 2000. ⇒170
- [44] J. Pennington, R. Socher,and C. Manning. GloVe: Global vectors for word representation. In EMNLP, pages 1532–1543, 2014. ⇒178
- [45] L. Philips. Hanging on the metaphone. Computer Language Magazine, 7(12):38, December 1990. ⇒178
- [46] L. Philips. The double metaphone search algorithm. C/C++ Users Journal, 18(6), June 2000. ⇒178
- [47] J. Pons, T. Lidy,and X. Serra. Experimenting with musically motivated convolutional neural networks. In CBMI, pages 1–6. IEEE, 2016. ⇒161
- [48] R. Priedhorsky, J. Chen, S. T. K. Lam, K. Panciera, L. Terveen,and J. Riedl. Creating, destroying, and restoring value in Wikipedia. In Proceedings of the 2007 international ACM conference on Supporting group work, pages 259–268. ACM, 2007. ⇒160
- [49] S. Reddy and K. Knight. Unsupervised discovery of rhyme schemes. In ACL (Short Papers), pages 77–82. The Association for Computer Linguistics, 2011. ⇒172
- [50] G. Salton, A. Wong, and A. C. S. Yang. A vector space model for automatic indexing. Communications of the ACM, 18:229–237, 1975. ⇒159, 171
- [51] H. Schreiber. Improving genre annotations for the million song dataset. In M. Müller and F. Wiering, editors, ISMIR, pages 241–247, 2015. ⇒170
- [52] F. Sebastiani. Machine learning in automated text categorization. ACM Computing Surveys, 34(1):1–47, 2002. ⇒174
- [53] S. Sigtia and S. Dixon. Improved music feature learning with deep neural networks. In ICASSP, pages 6959–6963. IEEE, 2014. ⇒161
- [54] A. Singhi and D. G. Brown. Are poetry and lyrics all that different? In H.-M. Wang, Y.-H. Yang, and J. H. Lee, editors, Proceedings of the 15th International Society for Music Information Retrieval Conference, ISMIR 2014, Taipei, Taiwan, October 27–31, 2014, pages 471–476, 2014. ⇒161
- [55] B. L. Sturm. An analysis of the gtzan music genre dataset. In Proceedings of the second international ACM workshop on Music information retrieval with user-centered and multimodal strategies, pages 7–12. ACM, 2012. ⇒160
- [56] B. L. Sturm. A survey of evaluation in music genre recognition. In International Workshop on Adaptive Multimedia Retrieval, pages 29–66. Springer, 2012. ⇒161
- [57] A. Swartz. MusicBrainz: a semantic Web service. IEEE Intelligent Systems, 17(1):76–77, 2002. ⇒164
- [58] A. Tsaptsinos. Lyrics-based music genre classification using a hierarchical attention network. In ISMIR, pages 694–701, 2017. ⇒162
- [59] G. Tzanetakis and P. Cook. Musical genre classification of audio signals. IEEE Transactions on speech and audio processing, 10(5):293–302, 2002. ⇒160
- [60] E. Zangerle, M. Tschuggnall, S. Wurzinger, and G. Specht. Alf-200k: Towards extensive multimodal analyses of music tracks and playlists. In European Conference on Information Retrieval, pages 584–590. Springer, 2018. ⇒162