Abstract
In previous works, the authors presented a measure of similarity between melodies by identifying them with sequences of ordered vectors and using a clustering process based on fuzzy logics. Along the same line, we propose a measure of musical similarity between fragments of digital audio. We present the SpectroMap algorithm that allows us to detect the local maxima of the audio spectrogram representation (also known as constellation map) and we compared the similarity between different maps belonging to different audio excerpts. As a result, it is obtained a value that represents the resemblance between two musical products. This procedure could be used as a non-subjective tool in automatic plagiarism detection. To illustrate this method, three experiments have been carried out comparing different versions famous pop songs. The results point to the usefulness of the method, although this should be contrasted with an analysis of the human perception of this similarity.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algoritms. Plenum Press, New York (1981)
Bezdek, J.C., Keller, J., Krisnapuram, R., Pal, N.: Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. Kluwer Academic Publishers, Boston, London, Dordrecht (1999)
Gan, G., Ma, C., Wu, J.: Data Clustering: Theory, Algorithms, and Applications. SIAM, Philadelphia (2007)
De Prisco, R., et al.: Music plagiarism at a glance: metrics of similarity and visualizations. In: 21st International Conference Information Visualisation (IV), pp. 410–415. IEEE, London (2017). https://doi.org/10.1109/iV.2017.49
Dunn, J.C.: A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J. Cybern. 3, 32–57 (1973)
Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice-Hall, Inc., Hoboken (1988)
Liern, V.: Fuzzy tuning systems: the mathematics of musicians. Fuzzy Sets Syst. 150(1), 35–52 (2005)
MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 3, pp. 281–297. Oakland, USA (1967)
Martínez, B., Liern, V.: A fuzzy-clustering based approach for measuring similarity between melodies. In: Agustín-Aquino, O.A., Lluis-Puebla, E., Montiel, M. (eds.) MCM 2017. LNCS (LNAI), vol. 10527, pp. 279–290. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-71827-9_21
Martínez–Rodríguez, B., Liern, V.: Mercury: a software based on fuzzy clustering for computer-assisted composition. In: Montiel, M., Gomez-Martin, F., Agustín-Aquino, O.A. (eds.) MCM 2019. LNCS (LNAI), vol. 11502, pp. 236–247. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-21392-3_19
Martınez-Rodrıguez B.: El fuzzy clustering y la similitud musical: aplicación a la composición asistida por ordenador. Ph.D. thesis. Universidad Politécnica de Valencia, Valencia, Spain (2019). https://doi.org/10.4995/Thesis/10251/134056
Wang, A.: An industrial strength audio search algorithm. In: Ismir, vol. 2003, pp. 7–13 (2003)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
López-García, A., Martínez-Rodríguez, B., Liern, V. (2022). A Proposal to Compare the Similarity Between Musical Products. One More Step for Automated Plagiarism Detection?. In: Montiel, M., Agustín-Aquino, O.A., Gómez, F., Kastine, J., Lluis-Puebla, E., Milam, B. (eds) Mathematics and Computation in Music. MCM 2022. Lecture Notes in Computer Science(), vol 13267. Springer, Cham. https://doi.org/10.1007/978-3-031-07015-0_16
Download citation
DOI: https://doi.org/10.1007/978-3-031-07015-0_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-07014-3
Online ISBN: 978-3-031-07015-0
eBook Packages: Computer ScienceComputer Science (R0)