How do we understand, and define the identity of a great performer or an entire piece of music? Looking at research from some of the great musical thinkers of the 20th and 21st centuries, the discussion moves to the specifics of which musical parameters contribute to identity, and how we can best define and codify those parameters so that they’re available to existing AI systems. 馃幐 馃幑 馃 #Identity #AI
Too Much Music takes a look back at the music technology underpinning a startup co-founded by our hosts in the early 2010s. With the motto “Use Music to Find Music” , Clio Music was perhaps the first commercially available search engine to speak the language of music and has been a part of TiVo since 2012. Our hosts share insights from that experience as well as the roles they see music AI technology playing in the future. 馃 馃憘
We love music for how it makes us feel. But the two founders of PatternSonix want to discuss why we might also want to turn music into data. What do we lose? What might we gain? Our fearless hosts begin with music notation and dive into performance nuances, related brain research, and ultimately zero in on some music-centric approaches that work well for intelligent music systems. 馃幐 馃幑 馃幒 #MusicData
Teaching intelligent systems to understand sonic surprise is essential to useful AI behavior. Computer analysis of musical expectation codifies the emotional journeys we experience when listening to music. Understanding these emotional arcs with analysis algorithms is an important step in aligning technology and artistry for future innovation. 馃憘馃幎馃帶 #EmotionalAI #MusicalAlgorithms