Chapter 7: Statistics of Sound

7.1 Music as Data

Think of a melody as numbers: pitch, duration, timbre. Simple statistics reveal style and structure.

# Example melody in MIDI pitches
melody_pitches = [60, 62, 64, 65, 67, 69, 71, 72]
melody_pitches

7.2 Music Analysis with Pandas and Python

Spreadsheets are just tables: every row describes one song, every column stores the same kind of fact for each song (tempo, key, energy, …).
Why is that powerful?

• Consistency → quick math Uniform columns let us sort, filter, and average in a single line of code.
• Clarity → better questions Well-labeled tables make patterns jump out: which keys dominate? How fast is the typical Miles Davis tune?
• Automation → scale The Pandas library treats a CSV file like a programmable spreadsheet, so the same analysis works on twenty songs or twenty-thousand.

In the table below each row is a jazz classic; the columns capture five numeric (quantitative: countable values) or categorical (qualitative: descriptive categories) features:

• Title: song name (categorical)
• Artist: performer or ensemble (categorical)
• Tempo: beats per minute (numeric)
• Key: tonal center (categorical)
• Energy: 0 to 1 proxy for loudness, density, rhythmic drive (numeric)

You’ll compute summary stats and build three quick plots:

1. Histogram of tempo: see how fast most tunes sit.
2. Scatter of tempo vs energy: spot high-energy outliers.
3. Box-plot of tempo by key: compare common jazz keys side-by-side.