Generating Musical Synthesizer Patches with Machine Learning
Music producers spend a lot of time designing sounds with an electronic instrument called the synthesizer. To do this, they select “patches” which are configurations that are used to generate sounds. Generative machine learning algorithms have recently made it possible to generate synthesizer patches in the style of genres or artists automatically. Here I explain the theory and provide an open-source prototype implementation.
A synthesizer is an el ectronic musical instrument that generates audio signals. Synthesizers generate audio through methods including subtractive synthesis, additive synthesis, and frequency modulation synthesis. These sounds may be shaped and modulated by components such as filters, envelopes, and low-frequency oscillators. Synthesizers are typically played with keyboards or controlled by sequencers, software, or other instruments, often via MIDI. [Wikipedia]
Synthesizers are the backbone of modern music production, they give producers the power to generate a massive variety of sounds using a single device. While synthesizers were originally analog devices, it is more common today to use digital simulations due to their convenience and increased flexibility.
