Lina Campanella

About Me

Lina Campanella is a master’s student and researcher in the Computer Music and Neural Audio Systems team within the Audio Communication Group at Technische Universität Berlin. Alongside her role as studio tutor, she co-developed the university's course on deep learning for audio data. Her current research focuses on conditioned neural modeling of a prepared EMT-140 plate reverb, exploring data-driven approaches to nonlinear electroacoustic systems. Guided by the view that neural networks should augment rather than automate creativity, her work aims to build responsive, artist-first tools.

Sessions

  • From Trained Model to Real-Time Implementation

    A Practical Guide to Neural Network Inference and Common Pitfalls
    14:00 - 17:00 UTC | Monday 9th November 2026 | SS Great Britain
    Intermediate
    Advanced
    In-Person Only

    Training a neural network for audio is a well-documented process. However, running it inside a deterministic callback that must return within a few milliseconds is not. This workshop addresses this issue. Starting with the requirements that a model must satisfy for real-time operation to be possible, such as streamability without boundary artefacts, explicit handling of state and a graph that the runtime can execute, as well as having an inference time below the deadline, the workshop then takes a pretrained model through export, benchmarking, integration and optimisation using ANIRA — an open-source library for real-time safe neural network inference. The […]