Week 4: Analysing encoded music notation

Task 1: Generating a jSymbolic report

Firstly, I worked with jSymbolic to extract some musical features. The first 11 features presented in the table are the ones recommended for this task. The rest of the features related to pitch, rhythm and tempo were selected by me since I found them worthy to include in my analysis. The jSymbolic report can be seen below or downloaded as a CSV here.

Feature name	Feature value
Number of Pitches	17.0
Number of Pitch Classes	7.0
Range	36
Strong Total Centres	2.0
Mean Pitch	56.0
Mean Pitch Class	6.657
Most Common Pitch	68.0
Most Common Pitch Class	6.0
Interval Between Most Prevalent Pitches	2.0
Pitch Variability	11.52
Most Common Melodic Interval	2.0
First Pitch	40.0
First Pitch Class	4.0
Last Pitch	71.0
Last Pitch Class	11.0
Total Number of Notes	548.0
Initial Tempo	112.0
Mean Tempo	112.0
Tempo Variability	0.0

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Task 2: Generating a piano roll and a pitch histogram

Secondly, I worked with a Python library called music21 to generate two visualisations. The graph on the left (Piano roll) visualises how pitches evolve over time whereas the graph on the right (Pitch histogram) visualises pitch count.