Temporal Analysis and Visualisation of Music
This paper proposes a temporal analysis for music metadata using a generative probabilistic model for collections the discrete datasets such as text corpora. This method is also a topic model that is used for discovering abstract topics from a collection of documents. The method is then applied to audio metadata and song lyrics extracted with Echo Nest® engine, Spotify® Lyrics Genius® API. Song data time series are generated by grouping data items by release date, genre and dominant topics (from LDA analysis). Using a technique from Network Theory we visualise how these topics, in this case, genres, are related to each other through time.
Bird, S., K. E. . L. E. (2009). Natural language processing with python. O’Reilly Media Inc.
Blei, M., N. A. . J. M. (2003). Latent dirichlet allocation. Journal of machine Learning research, pages 993–1022.
Chung, Y.; Glass, J. (2018). Speech2vec: A sequence-to-sequence framework for learning word embeddings from speech. arXiv preprint arXiv.
Dakshina, K., . S. R. (2013). Lda based emotion recognition from lyrics. Advanced Computing, Networking and Informatics, 1:187–194.
Devlin, J., C. M. L. K. T. K. (2019). Bert: Pre-training of deep bidirectional transformers for language understanding. Proceedings of NAACL.
Fan, J., . C. M. (2013). Study of chinese and uk hit songs prediction. Proceedings of International Symposium on Computer Music Multidisciplinary Research, pages 640– 652.
Fellbaum, C. (2005). Wordnet and wordnets. Encyclopedia of Language and Linguistics, 2nd ed, pages 665–670. for Developers., S. Get audio features for a track. Available at https: //developer.spotify.com/documentation/web-api/reference/ tracks/get-audio-features/ (2019/03/11).
Hoffman, M., B. F. R. . B. D. M. (2010). Online learning for latent dirichlet allocation. Advances in neural information processing systems.
Hu, J. (2009). Latent dirichlet allocation for text, images, and music. University of California, San Diego.
Johnson-Roberson, C., . J.-R. M. (2013). Temporal and regional variation in rap lyrics. NIPS Workshop on Topic Models: Computation, Application and Evaluation.
Kamada, T. K. S. (1989). An algorithm for drawing general undirected graphs. Information processing letters, 31(1):7–15.
Moura, L., F. E. S.-V. F. M. (2020). Music topics and metadata. https://data. mendeley.com/datasets/3t9vbwxgr5/1.
Pedregosa, F. (2011). Scikit-learn: Machine learning in python. Journal of machine Learning research, 12:2825–2830.
Pennington, J., S. R. . M. C. (2014). Glove: Global vectors for word representation. Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pages 1532–1543.
Pichl, M., Z. E. . S. G. (2016). Understanding playlist creation on music streaming platforms. IEEE International Symposium on Multimedia (ISM), pages 475–480.
Prim, R. C. (1957). Shortest connection networks and some generalizations. The Bell System Technical Journal, 36(6):1389–1401.
Salton, G. and McGill, M. J. (1986). Introduction to modern information retrieval. McGraw-Hill, Inc., USA.
Sasaki, S., Y. K. N. T. G. M. . M. S. (2014). Lyricsradar: A lyrics retrieval system based on latent topics of lyrics. Ismir.
Schulkin, J. and Raglan, G. B. (2014). The evolution of music and human social capability. Frontiers in Neuroscience.
Sharma, Govind, . N. M. (2011). Mining sentiments from songs using latent dirichlet allocation. International Symposium on Intelligent Data Analysis., pages 328–339.
Shuyo, N. (2010). Language detection library for java.