My words are my own, and if I say something unique those are my own words. But that is within the limitations of English language as an expressive form of spoken/written sounds. Take Shakespearean writing for example, regarded one of the greatest poets and writers of his time. He made up a lot of words cause he lacked expression within the limitations of the language, that's true creative freedom. If I take time and effort to write and think about my words I'm single threading output, but multithreading input. There's a plethora of different ways to say the same thing. The problem with AI and IP is that it creates this gray goo of information. Advanced statistics outputting remixes of creative input. The time of thought and the time of production taken into account of a multitude of information sources and writing styles, innovation in language and creative pursuits have led us to an overload of information. Who has and wants to take the time to slow down and tak...
Good gracious, I just found some more data, the Nottingham Database , which is a collection of ABC formatted music files. This format can be put into MIDI format and vice versa. Of course I'm facing a problem, first of all, the specific data I want, MIDI with a lot of different genres, is not widely available. Therefore I have a couple of options: Try to train on actual MP3/OGG/WAV/FLAC music files, which is going to take forever. Although the FMA data set offers 30s samples of the whole collection of songs. NSynth is a collection of single instruments, which is mostly suitable for synthesizing intstruments and not especially for generating songs/music. The Nottingham Database, an ABC formatted data base. The most suitable solution comes in the ABC formatted database, there is more to find and I'm currently tracking down more data. However, there are some caveits along the way. I have found papers using all of these data sets, therefore it is very likely t...
As seen in this updated mindmap below, there is a lot going on internally when making music, but there also is emotional influence of the listener, either intended or not, by the maker. This makes for a more wholesome structure of the research field and includes all the different parts that make music in itself an interesting thing to study. When looking into the technical details of making an artificial music generator there is a part which analyses data, implements learned details (which melodic and song structure are made up of) and the actual generator part which uses the aforementioned learned details and rules to generate music. In a set up with GANs there is the possibility to generate more data with the encoder, while the decoder is fed this information to discriminate between. The encoder is therefore atuned to generate different types of subsets and learns better what the difference with the original data set is. The sequential aspect of music ma...
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