AES Show 2024 NY

This paper presents a novel generative delay effect that utilizes generative AI to create unique variations of a melody with each new echo. Unlike traditional delay effects, where repetitions are identical to the original input, this effect generates variations in pitch and rhythm, enhancing creative possibilities for artists. The significance of this innovation lies in addressing artists' concerns about generative AI potentially replacing their roles. By integrating generative AI into the creative process, artists retain control and collaborate with the technology, rather than being supplanted by it. The paper outlines the processing methodology, which involves training a Long Short-Term Memory (LSTM) neural network on a dataset of publicly available music. The network generates output melodies based on input characteristics, employing a specialized notation language for music. Additionally, the implementation of this machine learning model within a delay plugin's architecture is discussed, focusing on parameters such as buffer length and tail length. The integration of the model into the broader plugin framework highlights the practical aspects of utilizing generative AI in audio effects. The paper also explores the feasibility of deploying this technology on microcontrollers for use in instruments and effects pedals. By leveraging low-power AI libraries, this advanced functionality can be achieved with minimal storage requirements, demonstrating the efficiency and versatility of the approach. Finally, a demonstration of an early version of the generative delay effect will be presented.