'Signal to Noise Loops 3++ ', the Irish Sound Science and Technology Concerts, Ulster University, Derry

Performed at the Signal to Noise Loops 3++ at the Irish Sound Science and Technology Association Concert in Derry.

Performance Notes:

Signal to Noise Loops: 3++ is live data-driven musical performance for laptop and guitar. The performance will be realised with the ‘PerformIoT’ system. PerformIoT is a simple system written in Python for leveraging IoT data in live electronic music performance. PerformIoT has grown out of work undertaken to sonify IoT network data from Pervasive Nation, a nationwide IoT network test bed operated by CONNECT, the Science Foundation Ireland Research Centre for Future Networks headquartered at Trinity College Dublin. PerformIoT retrieves data from the relevant API and maps it to OSC for use in a live performance setting. The first use for the frameworks was for the piece Noise Loops for Laptop, Improvised Electric Guitar and Dublin City Noise Data. This was performed at the 2017 Sonic Dreams Festival. In this piece IoT data from sensors measuring ambient noise levels around Dublin city was mapped to control performance parameters of a live electric guitar improvisation. The data was mapped to control the timbre of the guitar utilizing a multiband distortion to morph the sound. The data was also mapped to control advanced buffer, delay and filtering processing of the performance. It also controlled the synthesis of percussive elements within the performance. The system was expanded and adapted after the first performance and a different version of the system will be used at a second performance is scheduled for xCoAx in Madrid in July. A third performance will be taking place in Dublin in August. Updated iterations of the systems have been developed for each performance. The first three performances with the system used Smart City data to generate musical content. This was informed by the recent re-emergence of Lefebvre’s Rhythmanalysis in discourses around the Smart City. The version of the system to be used at ISSTA 2018 will represent a return to the original concept, drawing data drawn from Pervasive Nation. Network traffic data mapped to control parameters of a generative music system. This generative music system is responsive to both the original data and to the performer who will be interacting with the system live through an electric guitar. This system allows the player to explore and highlight interesting patterns in the data by way of musical interaction . This system involves the application of techniques and concepts from the field of data-driven music to achieve a balanced co-ordination between generative music composition and improvisation in the context of live laptop music performance. The tasks of data acquisition and preparation as well as the mapping of data to MIDI is carried out by a bespoke Python script. From there the data is sent to Ableton Live 10 where Max for Live is used to convert the guitar signal midi and both the data and the guitar signal are fed into generative bespoke music system to control and modulate parameters concerned with the generation of MIDI information, the musical organisation of that information and its mapping to synthesis parameters. The point of mapping data to sound, and more specifically IoT data, is to leverage some of the interesting patterns that present themselves across data streams/sets of this manner. Data-driven music is different from sonification where the point is to faithfully communicate or represent the data to the listener. Data-driven music is closer in many ways to generative music composition than it is to sonification because of its focus on finding patterns in the data that might be interesting when mapped to sonic and musical parameters. My previous data-driven music work has employed algorithmic composition techniques and dealt with used from the global financial crash. More recently I have begun to work with IoT data as I believe that the kinds of data we choose to measure and our reasons for measuring them say a lot about what a society values, cares about and finds interesting while the specific data measurements chronicle the complex interactions between people, the technologies they create and the worlds in which those people and technologies are situated. While these explicit points of information may not be directly represented in a performance the rich interleaved patterns of interaction between people, place and technology are transposed into the sonic realm in each performance. While more abstract and implicit in nature it is the aesthetic dimensionality of these interlocked patterns, which is of interest to me.