Neural Nets for Generating Music
Algorithmic music composition has developed a lot in the last few years, but the idea has a long history. In some sense, the first automatic music came from nature: Chinese windchimes, ancient Greek wind-powered Aeolian harps, or the Japanese water instrument suikinkutsu. But in the 1700s music became “algorithmic”: Musikalisches Würfelspiel, a game that generates short piano compositions from fragments, with choices made by dice.
Dice games, Markov chains, and RNNs aren’t the only ways to make algorithmic music. Some machine learning practitioners explore alternative approaches like hierarchical temporal memory, or principal components analysis. But I’m focusing on neural nets because they are responsible for most of the big changes recently. (Though even within the domain of neural nets there are some directions I’m leaving out that have fewer examples, such as restricted Boltzmann machines for composing 4-bar jazz licks, short variations on a single song, or hybrid RNN-RBM models, or hybrid autoencoder-LSTM models.)