Longxiang Jiao

Florian Grötschla, Longxiang Jiao, Luca A. Lanzendörfer et al.

We introduce Panama, an active learning framework to train parametric guitar amp models end-to-end using a combination of an LSTM model and a WaveNet-like architecture. With \model, one can create a virtual amp by recording samples that are determined through an ensemble-based active learning strategy to minimize the amount of datapoints needed (i.e., amp knob settings). Our strategy uses gradient-based optimization to maximize the disagreement among ensemble models, in order to identify the most informative datapoints. MUSHRA listening tests reveal that, with 75 datapoints, our models are able to match the perceptual quality of NAM, the leading open-source non-parametric amp modeler.

Longxiang Jiao, Lukas Hofmann, Yiru Yang et al.

While micro-scale traffic simulations provide essential data for urban planning, they are rarely coupled with the high-fidelity visualization or auralization necessary for effective stakeholder communication. In this work, we present a real-time 4D visualization framework that couples the SUMO traffic with a photorealistic, geospatially accurate VR representation of Zurich in Unreal Engine 5. Our architecture implements a robust C++ data pipeline for synchronized vehicle visualization and features an Open Sound Control (OSC) interface to support external auralization engines. We validate the framework through a user study assessing the correlation between simulated traffic dynamics and human perception. Results demonstrate a high degree of perceptual alignment, where users correctly interpret safety risks from the 4D simulation. Furthermore, our findings indicate that the inclusion of spatialized audio alters the user's sense of safety, showing the importance of multimodality in traffic simulations.

Florian Grötschla, Luca A. Lanzendörfer, Longxiang Jiao et al.

We introduce PANAMA, an active learning framework for the training of end-to-end parametric guitar amp models using a WaveNet-like architecture. With \model, one can create a virtual amp by recording samples that are determined by an active learning strategy to use a minimum amount of datapoints (i.e., amp knob settings). We show that gradient-based optimization algorithms can be used to determine the optimal datapoints to sample, and that the approach helps under a constrained number of samples.