Autocompose

Autocompose is the process of automatically generating musical material through algorithms or artificial intelligence with little or no human input. It can produce complete compositions or individual parts, such as melodies, harmonies, or rhythms, that fit predefined constraints like tempo, key, mode, and duration. Autocompose technologies are used to create background music for media, provide practice material for musicians, or serve as source material for further human editing and arrangement.

Techniques and tools employed in autocomposition range from traditional rule-based systems to probabilistic models and modern neural networks. Early approaches relied on Markov chains or generative grammars to produce sequences that followed statistical patterns. More recent systems use genetic algorithms to evolve musical ideas, recurrent neural networks and long short-term memory networks for sequence generation, and transformer models for more context-aware material. Outputs are typically generated as MIDI data or audio files, and are often integrated into digital audio workstations via plugins or standalone software.

Common applications include film and game scoring, ambient or cinematic soundtracks, educational and practice tools, and procedural content generation in interactive media. A typical workflow involves setting constraints (tempo, key, length, mood), generating material, evaluating and selecting outputs, and refining through tweaking or human editing.

Challenges for autocompose include ensuring musical coherence across longer forms, avoiding repetition, maintaining stylistic consistency with a given reference, and addressing issues of originality and licensing. As the field evolves, improvements in evaluation metrics and user control aim to make generated music more useful and trustworthy for composers and producers.