Using Data Science to Better Understand Taylor Swift’s Voice

I made a heatmap of Taylor Swift’s most frequently sung notes, calculated across all of her nine studio albums. A self-generated time series of each track’s isolated vocal frequencies was turned into a vector and grouped together by album, resulting in a matrix in the shape of M x N where N is the number of notes in the Grand Staff and M is the number of albums, with row-wise relative frequencies as the matrix elements. (This approach also powers the data within my iOS app, Karaokay.) The results demonstrate an artist whose mastery of songwriting spans multiple genres. Some analysis follows below the fold.

A visualization of Taylor Swift’s most commonly vocalized notes. Generated with Python using Seaborn.

1. Her most frequently vocalized notes are C4, D4, and E4. Fans invested in the debate over Taylor Swift’s voice type won’t find resolution here, but there may be some hints. While this chart shows that Taylor frequently sings the notes between G3 and C5, her entire discography indicates a range of approximately D3 to A5.

2. There is a trend reversal between 1989 and reputation. Until reputation, Taylor Swift pushed the average vocal frequency of her music higher with each new release. Following the reversal between 1989 and reputation is an equal and opposite trend culminating in evermore. We may see a similar reversal if (when?) Taylor releases a tenth album, based on the assumption of mean reversion.

3. Incremental change is the norm. There’s a pattern of incremental change between albums of similar styles, highlighted by the shift in note frequency from Fearless to Speak Now (country-pop), from Red to 1989 (pop), and from folklore to evermore (alternative). As Taylor Swift enters new sonic territories, she internalizes their unique qualities and explores them further in a follow-up release.

Psst… if you’re interested in seeing more interpretations of the data, you can follow along with the r/TaylorSwift thread.

Writer of code. Interested in data-driven policy. Graduate of @JacksonYale and @UCSBGlobal.