Icicles -- for piano, in the impressionist style, composed using MuseNet artificial intelligence скачать в хорошем качестве

Icicles -- for piano, in the impressionist style, composed using MuseNet artificial intelligence

A refreshing original work that is both stimulating and calming. This is why I like using the Rachmaninoff style in MuseNet, because it allows me to stretch well beyond the style of Rachmaninoff's music. Composers such as Leonard Bernstein sometimes subconsciously picked up themes from other composers, and composers like Rachmaninoff and Bartok and countless others were influenced by folk music. People scoff at the idea of computer generated music, like MuseNet produces, but MuseNet does not work by grabbing themes or phrases from the works of a composer. Like language models that predict the next word in a sequence, MuseNet predicts the next note. So, using MuseNet, you could generate a thousand first measures, and no two would be alike, nor would any of them be identifiable as a measure out of the composer's music. While working on a piece in the Chopin style, I produced an arpeggio that is very close to one found in Etude #1 Op.10 of Chopin, and I immediatley recognized it, because I know all of Chopin's piano works, and have played many of them, as bad as my piano playing is. But for the most part, MuseNet can generate almost anything, and it can be very surprising, such as music played so fast it sounds like noise, or something in Chopin's style that sounds exactly like Bach. But mostly, music generated in MuseNet's Chopin style, has at least elements or parts that are recognizeable as a general style, depending on how familiar you are with all of Chopin's works. The user/composer has unlimited choices for each measure of music in the piece. Working in the smallest tokens size, 50, allows very fine control by the composer, but I've found it is better to work in 100 and 220 tokens range, because you can better see the possibilities. You can switch these tokens sizes measure to measure. I sometimes generate a measure in 400, just to see where the AI might take something. I can always go back and re-generate in a smaller tokens size if I don't like it. MuseNet, unlike some other AI, is a collaboration. You don't have to be a musician or composer, or read music, to use MuseNet, and all you need is a love of music and a feeling for what sounds good, what measure sounds the best, as you build a piece, first measure to last. By going to the MuseNet blog page, you can see examples of what MuseNet creates when left to its own devices, which means if you were to pick the first measure that MuseNet generates for each measure of your piece, that would be total MuseNet. Parts of it can be great, but it frequently goes off the rails. That's where your individual choices can make a big difference in the continuity and overall structure of a piece. Musically, MuseNet's greatest stregth is its own ability to develop short themes and write harmony and counterpoint at a very high level. It is also unhibited, in the sense it can a work in a wild direction few people would think of. MuseNet's weakness is in spinning beautiful melodies. MuseNet is a free webpage based software which runs on GPT-2 artificial intelligence by OpenAI Labs. Christine Payne, whom I believe was the primary AI researcher who wrote MuseNet, is also a concert pianist. I'm pretty sure she left OpenAI Labs to pursue a career in piano. Video credits: Video by DistillVideos on Pixabay Video by SteverAubenstine on Pixabay Video by Life-of-Vids on Pixabay MuseNet attribution: Payne, Christine. "MuseNet." OpenAI, 25 Apr. 2019, openai.com/blog/musenet