Scaling and compressing melodies using geometric similarity measures

Caraballo, Luis Evaristo; Díaz-Báñez, José Miguel; Rodríguez, F.; Sánchez-Canales, Vanesa; Ventura, Inmaculada

doi:10.1016/j.amc.2022.127130

Applied Mathematics and Computation

2022

DOI: 10.1016/j.amc.2022.127130

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Scaling and compressing melodies using geometric similarity measures

Luis Evaristo Caraballo

José Miguel Díaz-Báñez

F. Rodríguez

et al.

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Melodic Pattern Recognition and Similarity Modelling: A Systematic Survey in Music Computing

Velankar¹,

Kulkarni²

2022

JTCSST

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Musical pattern identification is crucial for various classification and retrieval applications in computational musicology. Feature learning is the basic task, and features act as a basis for the Pattern Recognition (PR). Selecting an appropriate approach is vital to the accuracy of the retrieval algorithms. This research gives a comprehensive review of approaches used for PR and similarity modelling. It systematically analyses various approaches for melodic feature identification and comparatively evaluates the work done in the literature in terms of software tools used, melodic pattern representations, and matching. The study discusses the benefits and limitations of various approaches along with the challenges to be addressed for melodic PR. Results show a wide variety of approaches for different music genres and applications. Further, analysis shows that statistical and symbolic approaches were being used predominantly, and deep learning approaches are gaining popularity in recent times.

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Melodic Pattern Recognition and Similarity Modelling: A Systematic Survey in Music Computing

Velankar¹,

Kulkarni²

2022

JTCSST

View full text Add to dashboard Cite

show abstract

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Scaling and compressing melodies using geometric similarity measures

Cited by 1 publication

References 19 publications

Melodic Pattern Recognition and Similarity Modelling: A Systematic Survey in Music Computing

Melodic Pattern Recognition and Similarity Modelling: A Systematic Survey in Music Computing

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