Valentin Ackva

Audio Software Developer

tanh-lab.com

About Me

Valentin Ackva is an independent audio software developer and consultant working on real-time AI inference for music and audio. He is also an external researcher at TU Berlin's Computer Music and Neural Audio Systems team.

Valentin develops plugins freelance for Baby Audio and runs the audio software agency tanh-lab, which he founded with Fares Schulz, building (neural) audio products for clients like Suture Sound Inc, Tonsturm, meltedsounds and Black Salt Audio. He co-maintains anira, an open-source C++ library for real-time-safe neural network inference.

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 […]