Mixtape: Songs For All These Beats (2017)

“Songs For…” is an annual mixtape project that began in 2009, where every installment has a theme tied to the music I’ve been listening to over the past 12-months.

2017’s “Songs For All These Beats” is available now, and can be STREAMED HERE.

About the Artwork

This year’s artwork explores the connections between music and data.


As a means for it to be shared and communicated, music has always had a need to be converted into data. These data are designed like language: Sheet music, for example, is designed in a way that allows a musician to translate its musical notation into sound in real-time as the data is read.

Tracks from Songs For All These Beats displayed as waveforms

Translating music into other data formats creates alternative ways for its information to be interpreted. Waveforms, depict an exact visual representation of the duration and volume of sounds. They can be read quicker than the music they represent can be played, and multiple waveforms can be compared to one another at the same time.


Physical copies of music can be data too, as demonstrated by Rutherford Chang’s project ‘We Buy White Albums’: a collection of previously owned copies of The White Album by The Beatles. The wear and tear of ownership reveals clues about the listener’s relationship with their music. Every copy is identical but yet wholly unique at the same time.

Tracks from Songs For All These Beats displayed as bars, made up of individual lines that represent each beat of the song.

Today, data has influence on music in many unseen ways. Algorithmic curation — the technology that drives software features such as automated playlists — considers song metadata like beats per minute, duration, and key in order to guide our listening habits and preferences.

It’s a nuanced shift that inverses the music/data relationship found in earlier examples. Where music once informed the data points documented in sheet music, here, the opposite is true: Data and analytics are now playing a key role in the composition and creation of our digital music experiences. It raises keys questions about the extent to which data-driven composition might one day drive the majority of our listening.

The artwork for this year’s mixtape is a data visualization of the music in its entirety.

Each song is represented by a bar, and each bar is comprised of individual lines. Each line represents a single beat within a song.

The length of a song can be read in two ways, by either a bar’s length or its rotation. The whole image can be read like a clock face, with the total runtime of the mix clocking in at 45 minutes and 19 seconds.

Play with an interactive version of the artwork and listen to the mix HERE.


01. D33J— Park (Tape Version)
Apple Music| Bandcamp | Spotify

02. Shingo Suzuki — Rebirth
Apple Music| Bandcamp | Spotify

03. O’Flynn — Spyglass
Bandcamp | Spotify

04. Glenn Astro — Computer Killer
Amazon | Apple Music | Bandcamp | Spotify

05. Floating Points — Myrtle Avenue
Amazon | Apple Music| Bleep| Spotify

06. Noname — Freedom (Interlude)
Apple Music| Bandcamp | Spotify

07. LION BABE — Treat Me Like Fire
Amazon | Apple Music| Bandcamp | Spotify

08. Eric Lau — The Gathering
Amazon | Apple Music| Bandcamp | Spotify

09. Julien Dyne (Feat. Parks) — Fallin’ Down
Amazon | Apple Music| Bandcamp | Spotify

10. Nicholas Payton — #BAMboula
Amazon | Apple Music| Bandcamp | Spotify

11. Les Sins (Feat. Nate Salman) — Why
Amazon | Apple Music| Bandcamp | Spotify

Archie Bagnall is a Designer, Writer, and In-House lead based in Southern California. He is currently President of AIGA Orange County

You can visit his website here: http://archiebagnall.co.uk/

Or subscribe to his infrequent newsletter here: http://eepurl.com/ckWBIT

Sr. Experience Designer ML/AI at Adobe. President Emeritus, AIGA Orange County