Download The Origins of DAFx and its Future within the Sound and Music Computing Field DAFX is an established conference that has become a reference gathering for the researchers working on audio signal processing. In this presentation I will go back ten years to the beginning of this conference and to the ideas that promoted it. Then I will jump to the present, to the current context of our research field, different from the one ten years ago, and I will make some personal reflections on the current situation and the challenges that we are encountering.
Download A Statistics-Driven Differentiable Approach for Sound Texture Synthesis and Analysis In this work, we introduce TexStat, a novel loss function specifically designed for the analysis and synthesis of texture sounds
characterized by stochastic structure and perceptual stationarity.
Drawing inspiration from the statistical and perceptual framework
of McDermott and Simoncelli, TexStat identifies similarities
between signals belonging to the same texture category without
relying on temporal structure. We also propose using TexStat
as a validation metric alongside Frechet Audio Distances (FAD) to
evaluate texture sound synthesis models. In addition to TexStat,
we present TexEnv, an efficient, lightweight and differentiable
texture sound synthesizer that generates audio by imposing amplitude envelopes on filtered noise. We further integrate these components into TexDSP, a DDSP-inspired generative model tailored
for texture sounds. Through extensive experiments across various
texture sound types, we demonstrate that TexStat is perceptually meaningful, time-invariant, and robust to noise, features that
make it effective both as a loss function for generative tasks and as
a validation metric. All tools and code are provided as open-source
contributions and our PyTorch implementations are efficient, differentiable, and highly configurable, enabling its use in both generative tasks and as a perceptually grounded evaluation metric.