SINEGON

OVERVIEW

The music industry generated $30bn globally in revenues in 2023, with $7.1bn spent on A&R and marketing alone. With such figures being spent on artist promotion & development, there is a huge risk of labels not being able to recoup their investments. However, in the music business, success is dependent on luck – to a significant extent. Executives and even the most seasoned A&Rs often do not know for sure which songs in a recording artist’s discography will guarantee profitability, even if the artist is already established. If the artist happens to be new to the music industry, their chances of achieving commercial success are even slimmer. But what if historical data could be used to determine – and even guarantee a hit record... EVERYTIME?

YEAR

2025

ROLE

Creative Direction

Design

Research

SERVICES

3D Modelling and Rendering

UI Design

About the project

Sinegon is a data visualisation device which allows musicians to easily identify similarities that exist between commercially successful songs across all genres. It does this by "listening" to music, analysing the sine waves and interpreting this data in the form of an easy-to-read chart... all in a matter of seconds.

Data to be analysed includes vocal layers (VOC), beats per minute (BPM), instruments detected (INS), track length (LEN), percussion instrument intensity (PER) and major notes (MAJ).

Each of these data points are represented as a vertex on a polygon (similar to player stats in a football video game)

Smooth Scroll
This will hide itself!

SINEGON

OVERVIEW

The music industry generated $30bn globally in revenues in 2023, with $7.1bn spent on A&R and marketing alone. With such figures being spent on artist promotion & development, there is a huge risk of labels not being able to recoup their investments. However, in the music business, success is dependent on luck – to a significant extent. Executives and even the most seasoned A&Rs often do not know for sure which songs in a recording artist’s discography will guarantee profitability, even if the artist is already established. If the artist happens to be new to the music industry, their chances of achieving commercial success are even slimmer. But what if historical data could be used to determine – and even guarantee a hit record... EVERYTIME?

YEAR

2025

ROLE

Creative Direction

Design

Research

SERVICES

3D Modelling and Rendering

UI Design

About the project

Sinegon is a data visualisation device which allows musicians to easily identify similarities that exist between commercially successful songs across all genres. It does this by "listening" to music, analysing the sine waves and interpreting this data in the form of an easy-to-read chart... all in a matter of seconds.

Data to be analysed includes vocal layers (VOC), beats per minute (BPM), instruments detected (INS), track length (LEN), percussion instrument intensity (PER) and major notes (MAJ).

Each of these data points are represented as a vertex on a polygon (similar to player stats in a football video game)

Smooth Scroll
This will hide itself!

SINEGON

OVERVIEW

The music industry generated $30bn globally in revenues in 2023, with $7.1bn spent on A&R and marketing alone. With such figures being spent on artist promotion & development, there is a huge risk of labels not being able to recoup their investments. However, in the music business, success is dependent on luck – to a significant extent. Executives and even the most seasoned A&Rs often do not know for sure which songs in a recording artist’s discography will guarantee profitability, even if the artist is already established. If the artist happens to be new to the music industry, their chances of achieving commercial success are even slimmer. But what if historical data could be used to determine – and even guarantee a hit record... EVERYTIME?

YEAR

2025

ROLE

Creative Direction

Design

Research

SERVICES

3D Modelling and Rendering

UI Design

About the project

Sinegon is a data visualisation device which allows musicians to easily identify similarities that exist between commercially successful songs across all genres. It does this by "listening" to music, analysing the sine waves and interpreting this data in the form of an easy-to-read chart... all in a matter of seconds.

Data to be analysed includes vocal layers (VOC), beats per minute (BPM), instruments detected (INS), track length (LEN), percussion instrument intensity (PER) and major notes (MAJ).

Each of these data points are represented as a vertex on a polygon (similar to player stats in a football video game)

Smooth Scroll
This will hide itself!