Generating Albums with SampleRNN to Imitate Metal, Rock, and Punk Bands
This is an incremental proof-of-concept for new music software in experimental genres, primarily relevant to musicians and producers.
The paper tackled generating music albums that imitate metal, rock, and punk bands using an unconditional SampleRNN model, creating six albums with raw audio and associated artwork and song titles, and demonstrated potential for machine-assisted production.
This early example of neural synthesis is a proof-of-concept for how machine learning can drive new types of music software. Creating music can be as simple as specifying a set of music influences on which a model trains. We demonstrate a method for generating albums that imitate bands in experimental music genres previously unrealized by traditional synthesis techniques (e.g. additive, subtractive, FM, granular, concatenative). Raw audio is generated autoregressively in the time-domain using an unconditional SampleRNN. We create six albums this way. Artwork and song titles are also generated using materials from the original artists' back catalog as training data. We try a fully-automated method and a human-curated method. We discuss its potential for machine-assisted production.