Abstract
Users curate music playlists for many purposes, including focus, enjoyment and therapy. Popular music streaming services generate playlists automatically which are constant in genre or mood. We propose a method to automatically create playlists dynamic in both the Arousal-Valence emotion space and the audio features of songs. Our playlist algorithm uses a two-stage approach to sequentially choose songs, employing a K-Nearest Neighbors (KNN) model to gather potential songs based on emotion and analyzing them with acoustic similarity metrics. To evaluate the effectiveness of various audio feature data, KNN parameters, and similarity metrics, we developed a testing protocol which generates playlists that traverse both Arousal-Valence and audio feature spaces. We define evaluation metrics to measure a playlist’s smoothness and evenness using the Pearson correlation coefficient between dimensions and the variance of steps between songs, respectively. Our algorithm successfully creates smooth and evenly-spaced playlists that transition cohesively in both mood and genre. We explore how the choice of audio feature data, similarity metric, and KNN parameters all have an effect on playlists’ smoothness and evenness across these two spaces.
S. Gaur—Work completed at Oregon State University.
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References
Allik, A., Thalmann, F., Sandler, M.: MusicLynx: exploring music through artist similarity graphs. In: Companion Proceedings of the The Web Conference 2018. WWW 2018, pp. 167–170. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva (2018). https://doi.org/10.1145/3184558.3186970
Bertin-Mahieux, T., Ellis, D.P.W., Whitman, B., Lamere, P.: The million song dataset. In: Klapuri, A., Leider, C. (eds.) Proceedings of the 12th International Conference on Music Information Retrieval. ISMIR 2011, pp. 591–596. International Society for Music Information Retrieval, Miami (2011). https://doi.org/10.5281/zenodo.1415820
Blood, D.J., Ferriss, S.J.: Effects of background music on anxiety, satisfaction with communication, and productivity. Psychol. Rep. 72(1), 171–177 (1993). https://doi.org/10.2466/pr0.1993.72.1.171
Bonnin, G., Jannach, D.: Automated generation of music playlists: survey and experiments. ACM Comput. Surv. 47(2), 1–35 (2014). https://doi.org/10.1145/2652481
Cardoso, L., Panda, R., Paiva, R.P.: MOODetector: a prototype software tool for mood-based playlist generation. In: Proceedings of the 2nd Portuguese National Symposium on Informatics. INForum 2011, Coimbra, Portugal (2011)
Chen, S., Moore, J.L., Turnbull, D., Joachims, T.: Playlist prediction via metric embedding. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD 2012, pp. 714–722. Association for Computing Machinery, New York (2012). https://doi.org/10.1145/2339530.2339643
Delbouys, R., Hennequin, R., Piccoli, F., Royo-Letelier, J., Moussallam, M.: Music mood detection based on audio and lyrics with deep neural net. In: Gómez, E., Hu, X., Humphrey, E., Benetos, E. (eds.) Proceedings of the 19th International Conference on Music Information Retrieval. ISMIR 2018, pp. 370–375. International Society for Music Information Retrieval, Paris (2018). https://doi.org/10.5281/zenodo.1492427
Deng, J.J., Leung, C.: Emotion-based music recommendation using audio features and user playlist. In: 6th International Conference on New Trends in Information Science, Service Science and Data Mining. ISSDM 2012, pp. 796–801. Institute of Electrical and Electronics Engineers, Taipei (2012)
Donnelly, P., Gaur, S.: Mood Dynamic Playlist: interpolating a musical path between emotions using a KNN algorithm. In: Ahram, T., Taiar, R. (eds.) Proceedings of the 9th International Conference on Human Interaction & Emerging Technologies: Artificial Intelligence & Future Applications. IHIET-AI 2022, vol. 23. AHFE Open Access (2022). https://doi.org/10.54941/ahfe100894
Donohoe, R., McNeely, T.: The effect of student music choice on writing productivity. Technical report ED448472, US Department of Education (1999)
Feldman, S.: Music affects productivity. Manag. Rev. 80(7), 6–7 (1991)
Flexer, A., Schnitzer, D., Gasser, M., Widmer, G.: Playlist generation using start and end songs. In: Bello, J.P., Chew, E., Turnbull, D. (eds.) Proceedings of the 9th International Conference on Music Information Retrieval. ISMIR 2008, pp. 173–178. International Society of Music Information Retrieval, Philadelphia (2008). https://doi.org/10.5281/zenodo.1418272
Flint, M.: The effects of music on physical productivity. Ph.D. thesis, The Ohio State University (2010)
Fox, J., Embrey, E.: Music – an aid to productivity. Appl. Ergon. 3(4), 202–205 (1972). https://doi.org/10.1016/0003-6870(72)90101-9
Kellogg, R.T.: Writing habits and productivity in technical writing. Technical report ED249502, US Department of Education (1982)
Kim, H.G., Kim, G.Y., Kim, J.Y.: Music recommendation system using human activity recognition from accelerometer data. Trans. Consum. Electron. 65(3), 349–358 (2019). https://doi.org/10.1109/TCE.2019.2924177
Lesiuk, T.: The effect of music listening on work performance. Psychol. Music 33(2), 173–191 (2005). https://doi.org/10.1177/0305735605050650
Liebman, E., Saar-Tsechansky, M., Stone, P.: DJ-MC: a reinforcement-learning agent for music playlist recommendation. In: Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems. AAMAS 2015, pp. 591–599. International Foundation for Autonomous Agents and Multiagent Systems, Istanbul (2015). https://doi.org/10.5555/2772879.2772954
Ludewig, M., Kamehkhosh, I., Landia, N., Jannach, D.: Effective nearest-neighbor music recommendations. In: Proceedings of the ACM Recommender Systems Challenge 2018. RecSys Challenge 2018. Association for Computing Machinery, New York (2018). https://doi.org/10.1145/3267471.3267474
Marimont, R.B., Sharpiro, M.B.: Nearest neighbour searches and the curse of dimensionality. IMA J. Appl. Math. 24(1), 59–70 (1979). https://doi.org/10.1093/imamat/24.1.59
Mayer-Benarous, H., Benarous, X., Vonthron, F., Cohen, D.: Music therapy for children with autistic spectrum disorder and/or other neurodevelopmental disorders: a systematic review. Front. Psychiatry 12, 643234 (2021). https://doi.org/10.3389/fpsyt.2021.643234
Meyers, O.C.: A mood-based music classification and exploration system. Master’s thesis, Massachusetts Institute of Technology, Cambridge, USA (2007)
Moscato, V., Picariello, A., Sperlí, G.: An emotional recommender system for music. IEEE Intell. Syst. 36(5), 57–68 (2021). https://doi.org/10.1109/MIS.2020.3026000
Nathan, K.S., Arun, M., Kannan, M.S.: EMOSIC - an emotion based music player for Android. In: Proceedings of the 17th IEEE International Symposium on Signal Processing and Information Technology. ISSPIT 2017, pp. 371–276. Institute of Electrical and Electronics Engineers, Bilbao (2017). https://doi.org/10.1109/ISSPIT.2017.8388671
Pampalk, E., Pohle, T., Widmer, G.: Dynamic playlist generation based on skipping behavior. In: Proceedings of the 6th International Conference on Music Information Retrieval. ISMIR 2005, pp. 634–637. International Society of Music Information Retrieval, Victoria (2005). https://doi.org/10.5281/zenodo.1414932
Panda, R., Redinho, H., Gonçalves, C., Malheiro, R., Paiva, R.P.: How does the Spotify API compare to the music emotion recognition state-of-the-art? In: Mauro, D.A., Spagnol, S., Valle, A. (eds.) Proceedings of the 18th Sound and Music Computing Conference. SMC 2021. Axea sas/SMC Network, Virtual, pp. 238–245 (2021). https://doi.org/10.5281/zenodo.5045100
Pauws, S., Verhaegh, W., Vossen, M.: Music playlist generation by adapted simulated annealing. Inf. Sci. 178(3), 647–662 (2008). https://doi.org/10.1016/j.ins.2007.08.019
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011). https://doi.org/10.48550/arXiv.1201.0490
Rosen, P., Quadri, G.J.: LineSmooth: an analytical framework for evaluating the effectiveness of smoothing techniques on line charts. IEEE Trans. Vis. Comput. Graph. 27(2), 1536–1546 (2021). https://doi.org/10.1109/TVCG.2020.3030421
Russell, J.A.: A circumplex model of affect. J. Pers. Soc. Psychol. 39(6), 1161–1178 (1980). https://doi.org/10.1037/h0077714
Sakurai, K., Togo, R., Ogawa, T., Haseyama, M.: Music playlist generation based on graph exploration using reinforcement learning. In: Proceedings of the IEEE 3rd Global Conference on Life Sciences and Technologies. LifeTech 2021, pp. 53–54. Institute of Electrical and Electronics Engineers, Nara (2021). https://doi.org/10.1109/LifeTech52111.2021.9391870
Sen, A., Popat, D., Shah, H., Kuwor, P., Johri, E.: Music playlist generation using facial expression analysis and task extraction. Annales Universitatis Mariae Curie-Sklodowska, sectio AI - Informatica 16(2), 1–6 (2017). https://doi.org/10.17951/ai.2016.16.2.1
Song, Y., Dixon, S., Pearce, M.T., Halpern, A.R.: Perceived and induced emotion responses to popular music: categorical and dimensional models. Music. Percept. 33(4), 472–492 (2016). https://doi.org/10.1525/mp.2016.33.4.472
White, K.N.: The effects of background music in the classroom on the productivity, motivation, and behavior of fourth grade students. Master’s thesis, Columbia College, Columbia, SC, USA (2007)
Yeo, I.K., Johnson, R.A.: A new family of power transformations to improve normality or symmetry. Biometrika 87(4), 954–959 (2000). https://doi.org/10.1093/biomet/87.4.954
Zamani, H., Schedl, M., Lamere, P., Chen, C.W.: An analysis of approaches taken in the ACM RecSys Challenge 2018 for automatic music playlist continuation. ACM Trans. Intell. Syst. Technol. 10(5), 1–21 (2019). https://doi.org/10.1145/3344257
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Gaur, S., Donnelly, P.J. (2024). Generating Smooth Mood-Dynamic Playlists with Audio Features and KNN. In: Johnson, C., Rebelo, S.M., Santos, I. (eds) Artificial Intelligence in Music, Sound, Art and Design. EvoMUSART 2024. Lecture Notes in Computer Science, vol 14633. Springer, Cham. https://doi.org/10.1007/978-3-031-56992-0_11
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