This is a piece that originated as Bach’s BWV 180 Schmücke dich, o liebe Seele, reprocessed by the coconet deep learning model. I split the chorale up into 32 1/16th note segments, which is what the model was built to handle. I zero’d out one of the voices, and had the model remake that voice to create a four voice chorale similar to the Bach. Then I did it again, masking a different voice each time.
First, I searched for generated chorales that the highest pitch entropy using the muspy framework. Muspy is a library that reads a variety of musical structures, and includes many functions that help convert music files to other formats. It includes a set of metrics that measure a variety of qualities of the music. In my case, I searched the 100 16 voice chorales for those that had the most entropy, which in my case was the 12th through 15th voices of chorale #90.
Then I took that four voice chorale and stretched it out to three times its length. Next, I looked for parts of the longer chorale that had interesting segments. This was done using a measure of pitches outside the root F major scale, but on small subsets of the chorale. I stretched the most interesting segments by up to 8 times their original length. This was done in order to linger on the leading tones, suspensions, and other Bach tricks to add suspense and interest.
Next, I arpeggiated the entire chorale using a mask, not unlike the deep learning technique of convolution, but in my case just masking parts of notes, so that it created an arpeggio effect.
Finally, I rendered the chorale using Csound and my microtonal slide Bosendorfer in George Secor’s Victorian Rational Well Temperament. I added a convolution with an impulse response file from Teatro Alcorcon in Madrid from Angelo Farina. I think it sounds sweet, kind of like a finger-picking guitar.
It’s not Bach. But then, neither am I.