This repository serves to summarize the artifacts that resulted from a student research project on the complexity of European art music (often summarized under the term classical music).
It contains the Python-script used to extract the necessary information from a given MIDI corpus. The script was run on a constructed corpus from the "DisklavierTM World"-Archives, which is public at: http://www.kuhmann.com/Yamaha.htm . The results of the feature extraction can be found in corpus/extracted_features.csv.
The methodology for determining the complexity of musical dimensions implements Schannon's entropy measure and is based on the work of Madsen and Widmer (2015):
TY - JOUR AU - Madsen, Søren AU - Widmer, Gerhard PY - 2015/07/15 SP - T1 - A complexity-based approach to melody track identification in MIDI files JO - Proceedings of the International Workshop on Artificial Intelligence and Music ER -
The chosen categorization of musical dimensions is based on the work of Conklin (2006):
TY - JOUR AU - Conklin, Darrell PY - 2006 DA - 2006/12/01 TI - Melodic analysis with segment classes JO - Machine Learning SP - 349 EP - 360 VL - 65 IS - 2 AB - This paper presents a representation for melodic segment classes and applies it to music data mining. Melody is modeled as a sequence of segments, each segment being a sequence of notes. These segments are assigned to classes through a knowledge representation scheme which allows the flexible construction of abstract views of the music surface. The representation is applied to sequential pattern discovery and to the statistical modeling of musical style. SN - 1573-0565 UR - https://doi.org/10.1007/s10994-006-8712-x DO - 10.1007/s10994-006-8712-x ID - Conklin2006 ER -