Planet Symphony: Encode Messages as Music

DEV Community

Planet Symphony

Created for DurHack 2026 with the theme of "Composition"! We were also heavily inspired by QRTs Challenge "Information Hidden in Plain Sight" as well as Marshall Wace's "New Worlds". Humans have been looking to the stars for thousands of years. Many have wondered what exists beyond our little planet, in the great unknown. We have broadcast ourselves far and wide with radio waves, radiation, and space junk… … oddly nobody has answered. Maybe we should change the modality of our contact. Perhaps new worlds might respond to the power of song. Video Demo Our Journey In our time at DurHack 2026, we wanted to explore the possibilities of encoding data as music. We delivered in creating a Transmitter and Receiver for interplanetary communication, with data being entirely encoded within the melody itself (no tricks). What does it do? Planet Symphony begins with a message (plaintext) and a choice of key and musical scale (e.g., F# major-pentatonic). Next, that message is encoded algorithmically into a melody within a set of configurated parameters (cluster size, octaves). This melody is then sent to the Transmitter for a waiting receiver to hear. The waiting Receiver is a trained model for identifying musical notes, which is then passed into the algorithmic decoder. As the encoder is deterministic, as long as no notes are missed by the receiver the original message should be decoded with zero error. during encoding a specific music scale is chosen (demo uses f# major pentatonic) and only those notes are selected from so all notes are diatonic (in the specified key). when encoding characters to musical phrases, smaller intervals are prefered to sound more scalic. a drone note of the root is used to add a grounding tone and allow melodies to be properly parsed by your ears. paired with a specific pattern of cortchets and quavers the melody sounds more organic and pleasant. What are we most proud of? Malachi: Creating a deterministic encoder and decoder which can effectively translate messages into song and back to messages. Sarunas: Translating the notes into a song and having it actually sound good. Matty: Creating a visual language to fit with the project. Jack: Training a CNN to accurately read a spectrogram and improving accuracy from pure audio analysis. See our github, https://github.com/sarunasbend/durhack26, and try it yourself! See our devpost for more information, https://devpost.com/software/bluestooth What we learned Data transfer methods are often focused on speed, efficiency, trying to be out of your attention. We learned how data transfer doesn't have to fit these boxes and maybe slowing down can benefit all of us.

Powered by Readybot.io