Fares Schulz

Researcher

Technische Universität Berlin

About Me

Fares Schulz leads the Computer Music and Neural Audio Systems research team within the Audio Communication Group at Technische Universität Berlin, and works as a freelance software developer. His current research focuses on novel applications of neural networks for creative audio effects and synthesis, particularly in real-time and mixed-signal contexts. He treats neural networks as one tool among many rather than a one-size-fits-all solution, and is investigating how they can be deployed alongside long-established methods such as DSP algorithms and analog circuitry. He also works on large-scale multichannel spatial audio systems, whose high computational cost calls for clustered audio servers.

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 […]

  • Real-Time Inference of Neural Networks

    A Practical Approach for DSP Engineers – Part II
    14:00 - 14:50 UTC | Wednesday 13th November 2024 | Bristol 3
    Intermediate

    Continuing our exploration of implementing neural network inference for real-time audio applications, we have expanded from our initial plugin example to a comprehensive library that simplifies the deployment and integration of neural networks in audio applications. In this talk, we discuss various aspects of our implementation. Since it is crucial to know whether inference engines exhibit real-time violations, we first quantify real-time violations within inference executions. Subsequently, we explore the integration of these engines in real-time audio environments, specifically addressing the challenges of running multiple instances simultaneously. To accomplish this, we use a static thread pool and, when available, host-provided […]