Roberto Alonso - Archon (2023) Hi-Res

Artist: Roberto Alonso, Marek Poliks, Christian Smith
Title: Archon
Year Of Release: 2023
Label: NEOS Music
Genre: Classical
Quality: FLAC 16/24 Bit (96 KHz / tracks+booklet)
Total Time: 59:02 min
Total Size: 330 MB / 1,1 GB
WebSite: Album Preview
Tracklist:Title: Archon
Year Of Release: 2023
Label: NEOS Music
Genre: Classical
Quality: FLAC 16/24 Bit (96 KHz / tracks+booklet)
Total Time: 59:02 min
Total Size: 330 MB / 1,1 GB
WebSite: Album Preview
1. Crossing the Iron Ocean
2. Mist-Crawler
Archon is an open source data interface. It provides an interactive layer to Demiurge, a deep learning audio synthesis engine developed by Marek Poliks and Roberto Alonso. Archon, working in concert with Demiurge, makes most of the music on this record.
Archon was developed with the belief that the future of music will replace (and is replacing) instruments and instrumentality with algorithmic verticalities capable of deploying and recombining the literal historical entirety of recorded audio according to affect-based macro-categories. Making music will need to change from a realtime experience to one of prompt-based audio generation, results management, and readymade conformity-driven refinement operations. In response, Archon facilitates a dynamic, behaviorally-adaptive, interpretive relationship between a musician and a data entity reinjecting intimacy, proximity, and instrumentality into the higher-level data management activities that will constitute the future of music-making.
Archon was developed with the belief that the future of music will replace (and is replacing) instruments and instrumentality with algorithmic verticalities capable of deploying and recombining the literal historical entirety of recorded audio according to affect-based macro-categories. Making music will need to change from a realtime experience to one of prompt-based audio generation, results management, and readymade conformity-driven refinement operations. In response, Archon facilitates a dynamic, behaviorally-adaptive, interpretive relationship between a musician and a data entity reinjecting intimacy, proximity, and instrumentality into the higher-level data management activities that will constitute the future of music-making.