FFTimbre

In recent years, the accessibility of sound synthesis has significantly increased with the proliferation of standalone applications, plugins, and web-based tools offering a wide range of synthesis methods, including additive, FM, and wavetable synthesis. The advent of machine learning and AI-based algorithms has further advanced sound synthesis, particularly in commercially oriented synthesizers, by enabling the automatic replication of sounds through preset adjustments based on a selected target sound. In this paper, we introduce FFTimbre, an open-source Python toolbox designed to replicate sounds by analyzing their spectra and reproducing them using machine learning algorithms within highly customizable sound synthesis setups. We also present reference implementations for automated preset generation in Ableton’s Operator, alongside insights into LLM-assisted preset management. In addition to providing a detailed technical description of the toolbox, we explore its methodological implications in music education, emphasizing its utility as both an artistic and analytical tool, with reference to the theoretical work of Risset and Wessel (1999) .