In recent years I've dedicated myself to exploring the complex fields of Artificial Intelligence and Data Science. This research was driven by my curiosity to apply new techniques in music and develop my composition technique, while treating digital audio recordings not just as musical documents, but as data worthy of AI analysis.
A core aspect of this ongoing research is cluster analysis, a statistical method used for processing and organizing data. This methodology is intriguing when applied to music. By analyzing digital audio files, split into individual frames, and grouping these frames based on criteria like frequency, timbre, or intensity, we can glean insights into the audio material from a fresh perspective. This approach often results in an unusual blend of the seemingly absurd with a newfound coherence, as the algorithm uncovers hidden relationships within the data.
The primary aim of this process is to illuminate the various facets of audio material in novel ways. The outcomes of these algorithms provide a unique idiomatic character to the sounds, making the often abstract concepts of the digital world audibly tangible. This intersection of technology and art creates a distinctive aesthetic experience, one that is still being shaped and understood in its virtual context.
As this research unfolds, it leads to contemplative reflections on the interplay between automation and artistic creation. The increasing role of AI in artistic tasks is not just a technical advancement but also a philosophical and societal query. It compels us to question the essence and future of machine-generated art. Is machine-generated art still truly 'art'? What is the societal role of such art? Do we seek to retain a significant human element in the creative process, or should we venture towards new paradigms that challenge the traditional anthropocentric perspectives of art?