Download Compiling Differentiable Audio Graphs to Real-Time DSP Differentiable audio processors are habitually designed and optimised in machine-learning frameworks, but deploying them as real-time audio effects still often requires non-automatic implementation in a dedicated digital signal processing language. The translation is error-prone, demands an onerous verification process, and detaches research prototypes from usable production tools. That being so, we present ADAC, a compiler that lowers a trained model to a framework-agnostic intermediate representation and emits efficient FAUST code whose impulse response matches the source model to within floating-point arithmetic noise, direct paths included. The optimisation loop is made audible by replacing the model in a running plugin after each gradient step. The exported processor carries a small set of macro-controls that leave its stability intact. A stability certificate computed from the shipped parameters is checked before the plugin is built. At the demonstration, a feedback delay network is trained and exported to a working plugin.
Download ALAMODE: Automated Learning of Acoustical Modal Parameters via Differential Evolution This paper is a technical report on the methodology submitted for Task A of the 1st DAFx Parameter Estimation Challenge. The goal of the challenge’s task is to invert the multi-dimensional physical and geometric parameters of a virtual plate reverberator given a target reference impulse response. To achieve this, we present a multi-stage gradient-free optimization framework. This three-stage optimization is computed using an efficient physics-based simulator, starting with an optimization of only mode frequency-determining physical parameters, followed by a 6-DoF parameter optimization with position-determining ones and a final phase for frequency- and position-independent mode amplitude estimation.