A live music experience can be perceived as an exchange of ideas between the performer and the audience: the performer presenting their art and the audience consuming the art, responding through comprehension. Behind each of these actions is some profound, emotional intent to participate in the relationship of performer and audience. If we were to examine this relationship through the lens of data, a new perspective of live music surfaces.
Understanding the data exchange between performer and audience enriches the entire live music ecosystem.
As it is, there are many more entities within the live music business than just performer and audience. The process of showrunning directly involves entities such as booking agents, promoters, artist managers, venue owners, corporate sponsors, as well as the indirect entities such as streaming services, music hardware/software makers, etc. Each of these entities hold their specific role in how they interact with the rest of the live music ecosystem. Various transactional exchanges of information between the multiple entities must occur to facilitate the process — akin to the exchange of information between the performer and audience. Conversations within the ecosystem, such as booking agents talking to venue owners to see what type of music sells at their space, or corporate sponsors talking to labels for potential sponsorships on a certain artist’s tour, can all be channeled back to the interaction between an artist and an audience. By exploring the value of audience and artist interaction through quantification, we can illuminate potential innovations to existing infrastructure.
As a Not-Tomato-Lover might ask~
How might we achieve an open live music ecosystem by enriching data relationships?
Artist to Audience to Artist to Audience to…
The artist-audience relationship is paramount to live music; the business of live music relies on the intrinsic value created by this relationship. The relationship between artist and audience can be broken down into an exchange of fundamental gestures. Studying the performance of a DJ gives a clear and coherent example to understand the natural interactions in a performance. A DJ performance is a conversation between audience and artist: the DJ examines the crowd’s movements and energy to the music currently being played, interprets what they find to help plan and shape the mix, then repeats the process— a feedback loop of music into emotion-driven gestures, and emotion-driven gestures into music. In essence, sonic manipulation and biometric responses exist in a unique symbiotic relationship. Transforming these significant moments into data can ultimately uncover new forms of interaction within the dynamic live music experience.
Data: Coming of (a new) Age
As data flows from one entity to the next through organic processing and interaction, it is interpreted differently according to varying demands. For instance, analytics behind the incentives of emotional responses can uncover a new psychographic profile of the audience. Such a profile could be useful to various music companies outside of live music seeking market insight. New insights on audience attendees could help DJs accurately catalogue which songs best engage their specific audience, or tell a venue which songs tend to drive certain people to the bar. This data might also be used to affect the aesthetic environment in real time — for example, the space could turn blue when energy levels are low and red when energy levels are high. Through remixing data and building pipelines of data, we can discover a multitude of purposes for data that each entity can hold valuable.
This idea of dynamic data flow is characterized by Nomad (built by IDEO CoLab), an unique protocol for data streaming. Instead of a traditional model of query and response between server and client like in SQL, Nomad employs “nodes” that listen to data streams, augments the data, creates insights, and rebroadcasts the data. Rather than requesting data, a Nomad node taps into a constant flow of it.
LÜM, the next generation
In our last article, we explored the prototype of an autonomous DJ, named LÜM, that relies on the push of a user’s musical profile to the application for a machine reaction— a pipeline of data facilitating the conversation between artist and audience. Through building new pipelines of data flow in LÜM, we imagine a new live music experience, opening up new relationships and avenues of revenue, all based on data collected from the artist-audience relationship.