The Neuralacoustics Project: Exploring Deep-Learnin for Lightweight Numerical Modeling Synthesis
Övrigt konferensbidrag, 2022
Workshop, Session 1.
We present a newly started project called Neuralacoustics, aimed at using embedded AI to reduce the computational requirements of numerical modeling synthesis. To achieve this goal, we propose a novel design pipeline, in which neural networks are first trained off-line to approximate the behavior of target interactive acoustic systems, and then deployed on embedded and mobile platforms as real-time synthesis engines.
We present a newly started project called Neuralacoustics, aimed at using embedded AI to reduce the computational requirements of numerical modeling synthesis. To achieve this goal, we propose a novel design pipeline, in which neural networks are first trained off-line to approximate the behavior of target interactive acoustic systems, and then deployed on embedded and mobile platforms as real-time synthesis engines.
musical acoustics
deep learning
non-linear acoustics
machine learning
Författare
Victor Zappi
Northeastern University
Kivanc Tatar
Chalmers, Data- och informationsteknik, Data Science och AI
Embedded AI for NIME: Challenges and Opportunities Workshop at New Interfaces for Musical Expression
The International Conference on New Interfaces for Musical Expression
Auckland, New Zealand,
Auckland, New Zealand,
Styrkeområden
Informations- och kommunikationsteknik
Ämneskategorier
Inbäddad systemteknik