Systems and methods of procedural media generation

A data processing system for generating a rhythmic potentials vector, the system comprising:

• one or more processors;
• a memory;
• and
• a plurality of instructions stored in the memory and executable by the one or more processors to:
• receive as input a plurality of musical patterns, each musical pattern corresponding to a respective input attack vector;
• receive a plurality of rhythmic building blocks, each rhythmic building block comprising a respective set of time points corresponding to a stage of pattern formation in a musical meter;
• analyze each of the musical patterns to identify rhythmic building blocks which coincide with each musical pattern by identifying one or more symmetries between portions of each respective input attack vector and the respective set of time points of each rhythmic building block;
• generate an activations vector for each musical pattern, the activations vector comprising a respective activation number for each of the plurality of rhythmic building blocks, each respective activation number representing a respective fraction of time points in each rhythmic building block which corresponds to attacks in the respective input attack vector;
• and
• provide the activations vector as input to a machine learning model, the machine learning model configured to generate a rhythmic potentials vector for each musical pattern based on the respective activations vector, each rhythmic potentials vector comprising a respective likelihood of an attack at each time point in the respective musical pattern, wherein the respective likelihood of the attack at each time point is a function of a learned combination of activations associated with rhythmic building blocks which contain that time point.

A computer-implemented method of training a machine learning model for rhythmic pattern analysis and generation, the method comprising:

• receiving an input attack vector corresponding to a musical pattern,
• providing the input attack vector to a machine learning model;
• training the machine learning model to produce a set of address activations corresponding to rhythmic building blocks, wherein the address activations are configured such that an output of the machine learning model is a continuous-valued vector of rhythmic potentials, each rhythmic potential corresponding to a likelihood of an attack at a corresponding time point;
• applying a threshold to the rhythmic potentials to produce a reconstructed attack vector;
• comparing the reconstructed attack vector to the input attack vector;
• and
• adjusting one or more parameters of the machine learning model using a training method, thereby decreasing a difference between the reconstructed attack vector and the input attack vector.